2019 8 1 15 0

1 اثر تکانه قیمت نفت بر سیاست مالی صلاحدیدی در کشورهای عضو اوپک: رویکرد خود رگرسیون برداری ساختاری پانلی The Effects of Oil Price Shocks on Discretionary Fiscal Policy in Selected OPEC Countries: Panel Structural Vector Autoregressive https://ijes.shirazu.ac.ir/article_5029.html 10.22099/ijes.2018.30257.1470 1 The present study was aimed to investigate the effects of oil price shocks on discretionary fiscal policies in selected OPEC countries during 1980-2015. In this regard, the heterogeneous dynamic reaction to structural shock was examined using Panel Structural Vector Autoregressive (PSVAR) technique. Based on the findings, the effect of oil price shocks on discretionary fiscal policy was positive in short-run but ineffective in long-run. In addition, the oil price shocks caused an increase in inflation and government expenditure and a decrease in the economic growth in selected OPEC countries according to the Resource Curse phenomenon. Moreover, as variance decomposition showed, the government expenditure and economic growth have the most effect on discretionary policy changes. The effect of discretionary fiscal policy on economic growth in selected OPEC countries was negative, contrary to the Keynesian theory and the results of some other studies. Because discretionary fiscal policies play a major role in decisions of the countries mentioned above, the results also showed that a limitation in the government authority in OPEC countries would come into conflict with the decrease in economic growth and production fluctuation. 0 هدف مقاله حاضر بررسی اثر تکانه قیمت نفت بر سیاست های مالی صلاحدیدی در کشورهای عضو اپک طی دوره 2015-1980 می باشد. بدین منظور با استفاده از رویکرد نوین خود بازگشت برداری ساختاری پنل(Panel SVAR) که در پدرونی(2013) مطرح گردیده، به منظور برر سی واکنش های پویای ناهمگن به تکانه های ساختاری ، اثر تکانه قیمت نفت مورد بررسی قرار می گیرد. با توجه به نتایج پژوهش حاضر اثر تکانه نفتی بر سیاست مالی صلاحدیدی در کوتاه مدت مثبت اما در بلند مدت مطابق تئوری بی اثر می باشد. همچنین تکانه نفتی موجب افزایش تورم و مخارج دولت و نیز کاهش رشد اقتصادی مطابق پدیده نفرین منابع در کشورهای عضو اپک می گردد. همچنین در بررسی تجزیه واریانس بیشترین میزان تأثیرگذاری بر تغییرات سیاست های صلاحدیدی را متغیرهای مخارج دولت و رشد اقتصادی دارند. اما اثر سیاست های مالی صلاحدیدی بر رشد اقتصادی در کشورهای عضو اپک بر خلاف تئوری کینزین و مطابق برخی مطالعات صورت پذیرفته منفی می باشد. با توجه به این که سیاست های مالی صلاحدیدی نقش پررنگی در تصمیمات کشورهای مزبور ایفا میکنند، نتایج گواه بر محدودیت اختیارات دولت ها در کشورهای عضو اپک می باشد. 7 25 مرضیه دیندار رستمی Marzieh Dindar Rostami statistical center of Iran,Tehran, Iran. Iran marzieh.dindar@gmail.com شمس اله شیرین بخش Shamsollah Shirinbakhsh Faculty of Social and Economic, Alzahra University, Tehran, Iran. Iran sh_shirinbakhsh@yahoo.com زهرا افشاری Zahra Afshari Faculty of social and Economic, Alzahra University, Tehran, Iran. Iran afsharizah@gmail.com "رویکردPanel SVAR" "کشورهای عضو اوپک" "سیاست مالی صلاحدیدی" References##Attinasi, M., & Klem, M. A. (2016). The growth impact of discretionary fiscal policy measures. Journal of Macroeconomics, 49, 265–279.##Bank, A. (2011). Effects of discretionary fiscal policy: New empirical evidence for Germany. Hannover Economic Papers (HEP) dp-470, Leibniz Universität Hannover, Wirtschaftswissens chaftliche nakultät.##Beetsma. R., Giuliodori, M., & Klaassen, F. (2010). The effects of public spending shocks on trade balances and budget deficits in the European Union. Journal of the European Economic Association,6(2-3),414-423.##Beetsma, R. (2008). A survey of the effects of discretionary fiscal policy. Studier I Finans Politik, 2, 1-37.##Boiciuc, I. (2015). The cyclical behavior of fiscal policy in Romania. Procedia Economics and Finance, 32, 286 – 291.##Chalk, N. (2002). Structural balances and all that which indicators to use in assessing fiscal policy. IMF Working Paper, No. 02/101, 1-31, Available at SSRN: https://ssrn.com/abstract=879668##El-Anshasy, A., Bradley, A., & Michael, D. (2011). Oil prices and the fiscal policy response in oil-exporting countries. Journal of Policy Modeling, 34, 605-620.##Eltejai, E., & ArbabAfzali, M. (2012). Asymmetric effect of oil revenues on macroeconomic variables in Iran: Application of GARCH and SVAR models. Journal of Economic Development Research, 2(7), 89-110.##Fallahi, F., Asgharpour, H., Motefakerazad, M. A., & Montazeri Shurokhhalali, J. (2012). Influence of inflation on economic growth in Iran: Using slow transmission regression model (STR). Economic Studies and Policies, 8 (18), 47 - 64.##Fatas, A., & Miho, I. (2003). The case for restricting discretionary fiscal rules in the US states. Quarterly Journal of Economics, 90,101-117.##Gali, J., & Perotti, R. (2003). Fiscal policy and monetary integration in Europe. NBER Working Papers 9773, National Bureau of Economic Research, Inc.##Goes, C. (2016). Testing Piketty's hypothesis on the drivers of income inequality: Evidence from Panel VARs with heterogeneous dynamics. IMF Working Paper 16/160.##Hoghaberkiani, K., & Moradi, A. (2012). Estimation of potential production and production gap with emphasis on Hodrick-Prescott filter approach for Iran's economy (1367:1 1368:4). Journal of Economic Research, 7, 13, 143 - 168(In Persian).##Komijani, A., & Alavi, M. (1999). The effectiveness of monetary policies on inflation and economic growth in Iran. Journal of Management Studies, 11(4), 17-41.##Larch, M., & Salto, M. (2005). Fiscal rules, inertia, and discretionary fiscal policy, Applied Economics, 37(10), 1135-1146.##Marrewijk, c. v., & Verbeek, j. (1991). Growth, budget deficits, fiscal policies in an overlapping generation's model. Journal of Economics, 53(2), 185-203.##Mohammadi, H., & Baratzadeh, A. (2013). Impact of shocks caused by oil revenues on government expenditures and liquidity in Iran. Iranian Journal of Energy Economics, 2(7), 129-145.##Motevasseli, M., Ebrahimi, I., Shahmoradi, A., & Kajjani, A. (2010). Designing a new generation dynamic stochastic general equilibrium model for the Iranian economy as oil exporting country. Economic Research, 10(4), 9-15.##Nilly, M., & Amid, E. (1999). The effect of government fiscal policy on economic growth, proceedings of the 9th conference on monetary and foreign exchange policies. Monetary and Bank Research Institute, Central Bank of the Islamic Republic of Iran.##Pedroni, P. (2013). Structural panel VARs. Econometrics, 2, 180-206.##Rajaee, A. H., & Jalayi, A. (2017). The study of output gap in the Iranian economy using Hodrick-Prescott and Band-Post filtering. Journal of Economics, 3/4, 135-150.##Reis, L. d., Manasse, P., & Panizza, U. (2007). Targeting the structural balance. Research Department Publications 4507, Inter-American Development Bank, Research Department.##Samadi, S., YahyaAbadi, A., & Moallemi, N. (2009). Analysis of the impact of oil prices shocks on macroeconomic variables in Iran. Journal of Research and Economic Policy, 17(52), 26-5.##Samimi, A. (1997). Impact of government deficit on the growth of money and inflation in the Iranian economy (1981:1-1995:4). Master's thesis, Shiraz University.##Seyyedi, M., DaneshJafari, D., Bahrami, J., & Rafei, M. (2015). Providing a framework for optimum use of oil revenues in Iran: Dynamic stochastic general equilibrium (DSGE) approach. Planning, and Budget, 20(2), 21-58.##References##Attinasi, M., & Klem, M. A. (2016). The growth impact of discretionary fiscal policy measures. Journal of Macroeconomics, 49, 265–279.##Bank, A. (2011). Effects of discretionary fiscal policy: New empirical evidence for Germany. Hannover Economic Papers (HEP) dp-470, Leibniz Universität Hannover, Wirtschaftswissens chaftliche nakultät.##Beetsma. R., Giuliodori, M., & Klaassen, F. (2010). The effects of public spending shocks on trade balances and budget deficits in the European Union. Journal of the European Economic Association,6(2-3),414-423.##Beetsma, R. (2008). A survey of the effects of discretionary fiscal policy. Studier I Finans Politik, 2, 1-37.##Boiciuc, I. (2015). The cyclical behavior of fiscal policy in Romania. Procedia Economics and Finance, 32, 286 – 291.##Chalk, N. (2002). Structural balances and all that which indicators to use in assessing fiscal policy. IMF Working Paper, No. 02/101, 1-31, Available at SSRN: https://ssrn.com/abstract=879668##El-Anshasy, A., Bradley, A., & Michael, D. (2011). Oil prices and the fiscal policy response in oil-exporting countries. Journal of Policy Modeling, 34, 605-620.##Eltejai, E., & ArbabAfzali, M. (2012). Asymmetric effect of oil revenues on macroeconomic variables in Iran: Application of GARCH and SVAR models. Journal of Economic Development Research, 2(7), 89-110.##Fallahi, F., Asgharpour, H., Motefakerazad, M. A., & Montazeri Shurokhhalali, J. (2012). Influence of inflation on economic growth in Iran: Using slow transmission regression model (STR). Economic Studies and Policies, 8 (18), 47 - 64.##Fatas, A., & Miho, I. (2003). The case for restricting discretionary fiscal rules in the US states. Quarterly Journal of Economics, 90,101-117.##Gali, J., & Perotti, R. (2003). Fiscal policy and monetary integration in Europe. NBER Working Papers 9773, National Bureau of Economic Research, Inc.##Goes, C. (2016). Testing Piketty's hypothesis on the drivers of income inequality: Evidence from Panel VARs with heterogeneous dynamics. IMF Working Paper 16/160.##Hoghaberkiani, K., & Moradi, A. (2012). Estimation of potential production and production gap with emphasis on Hodrick-Prescott filter approach for Iran's economy (1367:1 1368:4). Journal of Economic Research, 7, 13, 143 - 168(In Persian).##Komijani, A., & Alavi, M. (1999). The effectiveness of monetary policies on inflation and economic growth in Iran. Journal of Management Studies, 11(4), 17-41.##Larch, M., & Salto, M. (2005). Fiscal rules, inertia, and discretionary fiscal policy, Applied Economics, 37(10), 1135-1146.##Marrewijk, c. v., & Verbeek, j. (1991). Growth, budget deficits, fiscal policies in an overlapping generation's model. Journal of Economics, 53(2), 185-203.##Mohammadi, H., & Baratzadeh, A. (2013). Impact of shocks caused by oil revenues on government expenditures and liquidity in Iran. Iranian Journal of Energy Economics, 2(7), 129-145.##Motevasseli, M., Ebrahimi, I., Shahmoradi, A., & Kajjani, A. (2010). Designing a new generation dynamic stochastic general equilibrium model for the Iranian economy as oil exporting country. Economic Research, 10(4), 9-15.##Nilly, M., & Amid, E. (1999). The effect of government fiscal policy on economic growth, proceedings of the 9th conference on monetary and foreign exchange policies. Monetary and Bank Research Institute, Central Bank of the Islamic Republic of Iran.##Pedroni, P. (2013). Structural panel VARs. Econometrics, 2, 180-206.##Rajaee, A. H., & Jalayi, A. (2017). The study of output gap in the Iranian economy using Hodrick-Prescott and Band-Post filtering. Journal of Economics, 3/4, 135-150.##Reis, L. d., Manasse, P., & Panizza, U. (2007). Targeting the structural balance. Research Department Publications 4507, Inter-American Development Bank, Research Department.##Samadi, S., YahyaAbadi, A., & Moallemi, N. (2009). Analysis of the impact of oil prices shocks on macroeconomic variables in Iran. Journal of Research and Economic Policy, 17(52), 26-5.##Samimi, A. (1997). Impact of government deficit on the growth of money and inflation in the Iranian economy (1981:1-1995:4). Master's thesis, Shiraz University.##Seyyedi, M., DaneshJafari, D., Bahrami, J., & Rafei, M. (2015). Providing a framework for optimum use of oil revenues in Iran: Dynamic stochastic general equilibrium (DSGE) approach. Planning, and Budget, 20(2), 21-58.##

1 بررسی تاثیر حقوق مالکیت بر کارآفرینی: شواهدی از برخی کشورهای منبع محور، کارایی محور و نوآوری محور The Effect of Property Rights on Entrepreneurship:Evidence from Some Factor-driven, Efficiency-driven, and Innovation-driven Countries https://ijes.shirazu.ac.ir/article_5181.html 10.22099/ijes.2019.31027.1492 1 Entrepreneurship is influenced by many factors and environments such as institutions. Institutions have an important role to play in the individual's tendency toward necessity and opportunity entrepreneurship. The purpose of this paper was to examine the impact of institutional quality (property rights) on opportunity and necessity entrepreneurship. The results, based on unbalanced panel data from 2005 to 2015, showed that property rights did not have a significant effect on the opportunity entrepreneurship in the factor-driven group while it had a negative impact on necessity entrepreneurship. In the efficiency-driven group, protecting property rights would provide the perfect context for opportunity entrepreneurship and reduce necessity entrepreneurship, but in the innovation-driven group, strengthening property rights increased both opportunity and necessity entrepreneurship. These results indicate that the impact of property rights on (opportunity- and necessity-) entrepreneurship depends on the level of economic development of countries. 0 کار آفرینی تحت تاثیر محیط ها و عوامل متعددی از جمله محیط نهادی است. نهادها نقش مهمی در تمایل افراد برای کار آفرینی فرصت و ضرورت بازی می کنند. هدف این مقاله شناسایی نقش و تاثیر کیفیت نهادی (از جمله حقوق مالکیت) در گسترش کارآفرینی ضرورت و فرصت است. نتایج تحقیق براساس داده های پانلی نامتوازن در دوره 2015-2005 نشان داد که حقوق مالکیت تاثیر معناداری بر کارآفرینی فرصت در کشورهای منبع محور ندارد؛ اما بر کارآفرینی ضرورت تاثیر منفی داشته است. همچنین در کشورهای کارایی محور حمایت از حقوق مالکیت زمینه مناسبی برای حمایت از کارآفرینی فرصت ایجاد کرده و کارآفرینی ضرورت را کاهش داده است. اما در گروه کشورهای نوآوری محور، تقویت حقوق مالکیت هر دو نوع کارآفرینی فرصت و ضرورت را افزایش داده است. این نتایج بیانگر این است که تاثیر حقوق مالکیت بر کارآفرینی (فرصت و ضرورت) به سطح توسعه اقتصادی کشورها وابسته است. 27 51 علی حسین صمدی Ali Hussein Samadi Department of Economics, Shiraz University, Shiraz, Iran. ایران asamadi@rose.shirazu.ac.ir سارا تقا Sara Togha Department of economics, Shiraz university, Shiraz, Iran. Iran saratogha@yahoo.com حقوق مالکیت کارآفرینی فرصت کارآفرینی ضرورت سطح توسعه داده های پانلی نامتوازن References##Acs, Z. J., & Amoros, J. E. (2008). Entrepreneurship and competitiveness dynamics in Latin America. Small Business Economics, 31(3), 305-322.##Acs, Z. J., Desai, S., & Hessels, J. (2008). Entrepreneurship, economic development and institutions. Small Business Economics, 31(3), 219-234.##Aidis, R., Estrin, S., & Mickiewicz, T. (2008). Institutions and entrepreneurship development in Russia: A comparative perspective. Journal of Business Venturing, 23(6), 656-672.##Aidis, R., Estrin, S., & Mickiewicz, T. (2009). Entrepreneurial entry: Which institutions matter?IZA Discussion Paper, 4123, 2-45.##Amoros-Espinosa, J. (2009). Entrepreneurship and Quality of Institutions. United Nations University, World Institute for Development Economics Research, 1-23.##Angulo-Guerrero, M. J., Perez-Moreno, S., & Abad-Guerrero, I. M. (2017). How economic freedom affects opportunity and necessity entrepreneurship in the OECD countries.Journal of Business Research,73, 30-37.##Aparicio, S., Urbano, D., & Audretsch, D. (2016). Institutional factors, opportunity entrepreneurship and economic growth: Panel data evidence.Technological Forecasting and Social Change,102, 45-61.##Autio, E., & Acs, Z. (2010). Intellectual property protection and the formation of entrepreneurial growth aspirations. Strategic Entrepreneurship Journal, 4(3), 234-251.‏##Baumol, W.J. (1990). Entrepreneurship: Productive, unproductive, and destructive. Journal of Political Economy, 98(5), 893-921.##Beheshti, M., Kheiravar, M., & Ghazvinian, M. (2009). Investigating the relation between entrepreneurship and unemployment in Iran's industrial sector.BeyondManagement Magazine, 3 (11), 153-183.##Bowen, H. P., & De Clercq, D. (2008). Institutional context and the allocation of entrepreneurial effort. Journal of International Business Studies, 39(4), 747-767.##Brixiova, Z., & Egert, B. (2017). Entrepreneurship, institutions and skills in low-income countries. Economic Modelling, 67, 381-391.##Carree, M., Van Stel, A., Thurik, R., & Wennekers, S. (2002). Economic development and business ownership: An analysis using data of 23 OECD countries in the period 1976-1996. Small Business Economics, 19(3), 271-290.##Castano, M. S., Mendez, M. T., & Galindo, M. A. (2015). The effect of social, cultural, and economic factors on entrepreneurship. Journal of Business Research, 68(7), 1496-1500.##Dau, L. A., & Cuervo-Cazurra, A. (2014). To formalize or not to formalize: Entrepreneurship and pro-market institutions. Journal of Business Venturing, 29(5), 668-686.##El-Harbi, S., & Anderson, A. R. (2010). Institutions and the shaping of different forms of entrepreneurship. The Journal of Socio-Economics, 39(3), 436-444.##Estrin, S., & Mickiewicz, T. (2010). Entrepreneurship in transition economies: The role of institutions and generational change.IZA Discussion Paper, 4805, 2-42.##Estrin, S., & Mickiewicz, T. (2011). Institutions and female entrepreneurship. Small Business Economics, 37(4), 397-415.##Estrin, S., Korosteleva, J., & Mickiewicz, T. (2013). Which institutions encourage entrepreneurial growth aspirations? Journal of Business Venturing, 28(4), 564-580.##Faria, J. R., Cuestas, J. C., & Mourelle, E. (2010). Entrepreneurship and unemployment: A nonlinear bidirectional causality?Economic Modelling, 27(5), 1282-1291.##Fuentelsaz, L., Gonzalez, C., Maicas, J. P., & Montero, J. (2015). How different formal institutions affect opportunity and necessity entrepreneurship. Business Research Quarterly, 18(4), 246-258.##Greenwood, R., & Suddaby, R. (2006). Institutional entrepreneurship in mature fields: The big five accounting firms. Academy of Management Journal, 49(1), 27-48.##Halicioglu, F. & Yolac, S. (2015). Testing the impact of unemployment on self-employment: Evidence from OECD countries. Social and Behavioral Science, 195, 10-17.##Harper, D. A. (2013). Property rights, entrepreneurship and coordination. Journal of Economic Behavior and Organization, 88, 62-77.##Henrekson, M. (2007). Entrepreneurship and institutions. Research Institute of Industrial Economics (IFN), 707, 1-28.##Henrekson, M., & Sanandaji, T. (2011). The interaction of entrepreneurship and institutions. Journal of Institutional Economics, 7(1), 47-75.##Herrera-Echeverri, H., Haar, J., & Estevez-Breton, J. B. (2013). Foreign direct investment, institutional quality, economic freedom and entrepreneurship in emerging markets. Journal of Business Research, 67(9), 1921-1932.##Isazade, S., & Mehranfar, J. (2012). The role of institutions in entrepreneurship formation in selected countries of the world. Economic Research Quarterly, 12 (44), 199-212.##Kalantaridis, C., & Fletcher, D. (2012). Entrepreneurship and institutional change: A research agenda. Entrepreneurship & Regional Development, 24(3-4), 199-214.##Kelley, D. J., Singer, S., & Herrington, M. (2012). Global Entrepreneurship Monitors 2011 Global Report. Global Entrepreneurship Research Association, London Business School.##Kobeissi, N. (2010). Gender factors and female entrepreneurship: International evidence and policy implications. Journal of International Entrepreneurship, 8(1), 1-35.##Koellinger, P., & Minniti, M. (2009). Unemployment benefits crowd out nascent entrepreneurial activity. Economics Letters, 103(2), 96-98.##Levie, J., & Autio, E. (2008). A theoretical grounding and test of the GEM model. Small Business Economics, 31(3), 235-263.##Marinov, M., & Marinova, S. (1996). Characteristics and conditions of entrepreneurship in Eastern Europe. Journal for East European Management Studies, 7-24.##Noorderhaven, N., Thurik, R., Wennekers, S. & Stel, A. V. (2004). The role of dissatisfaction and percapita income in explaining self-employment across 15 European countries. Journal of Entrepreneurship: Theory and Practice, 28(5), 447-466.##Nystrom, K. (2008). The institutions of economic freedom and entrepreneurship: Evidence from panel data. Public Choice, 136(3-4), 269-282.##Parker, S. C. & Robson, M. (2004). Explaining international variations in entrepreneurship: Evidence from a panel of OECD countries. Southern Economic Journal, 71(2), 287-301.##Pathak, S., Xavier-Oliveira, E., & Laplume, A. O. (2013). Influence of intellectual property, foreign investment, and technological adoption on technology entrepreneurship. Journal of Business Research, 66(10), 2090-2101.##Reynolds, P. D., Camp, S. M., Bygrave, W. D., Autio, E., & Hay, M. (2002). Global Entrepreneurship Monitor GEM 2001 Summary Report. London Business School and Babson College.##Rodrik, D., (2000). Institutions for high-quality growth: What they are and how to acquire them. Studies in Comparative International Development 35, 3–31.##Samadi, A. H. (2018). Institutions and Entrepreneurship in MENA Countries. In N. Faghih, &M. R. Zali (Editors), Entrepreneurship Ecosystem in the Middle East and North Africa (MENA): Dynamics in Trends, Policy and Business Environment, Springer International Publishing,53-93.##Samadi, A. H. (2019). Institutions and entrepreneurship: Unidirectional or bidirectional causality? Journal of Global Entrepreneurship Research, 9(1), 3-18.##Sambharya, R. & Musteen, M. (2014). Institutional environment and entrepreneurship: An empirical study across countries.Journal of Institutional Entrepreneurship, 12(4), 314-330.##Sautet, F. (2005). The role of institutions in entrepreneurship: Implications for development policy.Mercatus Center, George Mason University##Schumpeter, J. (1934). The Theory of Economic Development, Harvard University Press, Cambridge, MA.##Simon-Moya, V., Revuelto-Taboada, L., & Guerrero, R. F. (2014). Institutional and economic drivers of entrepreneurship: An international perspective. Journal of Business Research, 67(5), 715-721.##Smith, E. (2012). Explaining public entrepreneurship in local government organizations. State and Local Government Review, 44(3), 171-184.##Stephan, P. E., & Levin, S. G. (1996). Property rights and entrepreneurship in science. Small Business Economics,8(3), 177-188.##Stephen, F. H., Urbano, D., & Van Hemmen, S. (2005). The impact of institutions on entrepreneurial activity.Managerial and Decision Economics, 26(7), 413-419.##Torrini, R. (2005). Cross-country differences in self-employment rates: The role of institutions. Labor Economics, 12(5), 661-683.##Troilo, M. (2011). Legal institutions and high-growth aspiration entrepreneurship.Economic Systems, 35(2), 158-175.##Valdez, M. E.,& Richardson, J. (2013). Institutional determinants of macro-level entrepreneurship.Entrepreneurship: Theory and Practice, 37(5), 1149-1175.##Wennekers, S., Van Wennekers, A., Thurik, R. & Reynolds, P. (2005). Nascent entrepreneurship and the level of economic development. Small Business Economics, 24(3), 293-309.##Whiting, S. H. (2006). Power and wealth in rural China: The political economy of institutional change. Cambridge University Press.##Williams, N., & Vorley, T. (2015). Institutional asymmetry: How formal and informal institutions affect entrepreneurship in Bulgaria. International Small Business Journal, 33(8), 840-861.##Zali, M., & Razavi, M. (2012). Evaluation of Entrepreneurship Indicators in Iran Based on the Global Entrepreneurship Monitor Model.Labor and Social Security Institute,Report of the 5th Research Program.##http://data.worldbank.org##www.gemconsortium.org/data/key-aps##

1 پیش بینی تولیدات صنعتی در ایران: مقایسه شبکه های عصبی فازی و سیستم فازی عصبی تطبیقی Forecasting Industrial Production in Iran: A Comparative Study of Artificial Neural Networks and Adaptive Nero-Fuzzy Inference System https://ijes.shirazu.ac.ir/article_4878.html 10.22099/ijes.2018.28706.1438 1 Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features has made significant progress in recent years. The current study compared the accuracy of the Artificial Neural Networks (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) approaches to assess the current state and predict the future state of industrial production. The seasonal dataset comprised the labor force, capital stock, human capital, trade openness, liquidity and credit financing to the industrial sector as input variables and value added of industrial production as the output variable for the period of 1988 to 2018. The dataset was used to forecast industrial production for Seasons of the year 2019 and 2020. The results showed that, while both are appropriate tools for forecasting industrial production, ANFIS had a lower the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) than ANN. The findings of the research indicate that ANFIS is more effective in forecasting industrial production, which can help policymakers in planning and creating an effective strategy for the future. 0 Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features has made significant progress in recent years. The current study compared the accuracy of the Artificial Neural Networks (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) approaches to assess the current state and predict the future state of industrial production. The seasonal dataset comprised the labor force, capital stock, human capital, trade openness, liquidity and credit financing to the industrial sector as input variables and value added of industrial production as the output variable for the period of 1988 to 2018. The dataset was used to forecast industrial production for Seasons of the year 2019 and 2020. The results showed that, while both are appropriate tools for forecasting industrial production, ANFIS had a lower the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) than ANN. The findings of the research indicate that ANFIS is more effective in forecasting industrial production, which can help policymakers in planning and creating an effective strategy for the future. 53 68 افسانه کاظمی مهرآبادی Afsaneh Kazemi Mehrabadi Facolty of Economic,University of Mazandaran, Babolsar, Iran. Iran akazemi@modares.ac.ir وحید تقی نژاد عمران Vahid Taghinezhad Omran Facolty of Economic,University of Mazandaran, Babolsar, Iran. Iran omran@umz.ac.ir محمد ولی پور خطیر Mohammad Valipour Khatir Facolty of Economic,University of Mazandaran, Babolsar, Iran. Iran m.khatir1461@gmail.com سعید راسخی Saeed Rasekhi Facolty of Economic,University of Mazandaran, Babolsar, Iran. Iran saeed_rasekhi@yahoo.com پیش بینی تولیدات صنعتی شبکه های عصبی فازی سیستم فازی عصبی تطبیقی ایران References##Abbasi, GH. R. (2009). Convergence between Financial Development and Industrial Production in Iran. Economical Modeling, 3(7), 137-154.##Acedański, J. (2013). Forecasting industrial production in Poland – A comparison of different methods. Ekonometria Econometrics, 1(39), 40-51.##Assaf, N. A. (2011). Mercado financeiro. (10th ed.). So Paulo: Editora Atlas.##Atsalakis, G. S., Dimitrakakis E. M., and C. D. Zopounidis (2011). Elliot Wave Theory and neuro-fuzzy systems, stock market prediction: The WASP system. Expert Systems with Applications, 38, 9196– 9206.##Ayodele, A. A., Aderemi, O. A. and Charles, K. A. (2014). Stock Price Prediction Using the ARIMA Model, School of Mathematic, Statistics & Computer Science, 16th International Conference on Computer Modelling and Simulation, 105-111.##Azar, A. and Karimi, S. (2010). Neural Network Forecasts of Stock Return Using Accounting Ratios. Financial Research Journal, 11(28), 3-20.##Bakhtiari, S. and Salem, B. (2009). The effects of trade liberalization on the trade of products under the industrial sectors of Iran. Economics Research, 8(31), 15-27.##Central Bank website, statistics and data (www.cbi.ir).##Chen, M. Y. and Chen, B. T. (2015). A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences, 294, 227-241.##Chester, M. (1993). Neural networks: a tutorial. Prentice-Hall, Inc.##Fahim Yahyaee, F. and Falihi, N. (2003). The Impact of Monetary and Financial Policies on the Industry in the Last 25 Years. Economic Research, 3(8), 199-215.##Günay, M. (2018). Forecasting industrial production and inflation in Turkey with factor models. Central Bank Review, 18(4), 149-161.##Hadinejad, M. and Mehrabian, A. (2008). AN Examination of Credits Bank Facilities Effects on Irans Manufacturing Industry Growth. Journal of Management System (Financial Economics and Development), 1(2), 75 – 85.  ##Heravi, S., Osborn, D. R., and Birchenhall, C. R. (2004). Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting, 20(3), 435-446.##Hykin, S. (1994). Neural networks: A comprehensive foundation. New York: Macmillan.##Hykin, S. (1999). Neural Networks: A Comprehensive Foundation. Printice- Hall, Inc., New Jersey.##Jang, J. Sh. R. (1993). ANFIS: Adap tive-Ne twork-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665- 685.##Kangarani Farahani, M. and Mehralian, S. (2013). Comparison between Artificial Neural Network and Neuro-Fuzzy for Gold Price Prediction. 13th Iranian Conference on Fuzzy Systems (IFSC).##Kassem, Y., Çamur, H., and Esenel, E. (2017). ANFIS and Response Surface Methodology (RSM) prediction of biodiesel dynamic viscosity at 313 K. 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception.##Kavitha Mayilvaganan, M. and Naidu, K. B. (2011). Comparative Study of ANN and ANFIS for the Prediction of Groundwater Level of a Watershed. Global Journal of Mathematical Sciences: Theory and Practical, 3(4), 299-306.##Mirsoltani, S. M. and Niaki, S. T. A. (2013). Forecasting Energy Price and Consumption for Iranian Industrial Sectors Using ANN and ANFIS. Iranian Journal of Economic Studies, 2(1), 73-102.##Mohamad Alizadeh, A., Raei, R. and Mohammadi, Sh. (2015). Prediction of stock market crash using self-organizing maps. Financial Research Journal, 17(1), 159-178.  ##Monjazeb, M. R. (2000). Evaluation of the Effect of Liquidity on the Value Added of the Industrial Sector. Journal of Economic Research and Policies, 7(12), 39-59.##Murat, Y. S. and Ceylan, H. (2006). Use of artificial neural networks for transport energy demand modeling. Energy Policy, 34(17), 3165-3172.##Pablo-Romero, M. P., and Sanchez-Braza, A., (2015). Productive energy use and economic growth: energy, physical and human capital relationships. Energy Econ. 49, 420-429.##Pablo-Romero, M. P., Sanchez-Braza, A. and Exposito, A. (2019). Industry level production functions and energy use in 12 EU countries.##Raghuramg, R. and Luigi, Z. (2002).Financial system industrial structure and growth. Oxford review of Economic Policy, 17(4).##Samouel, B. and Aram, B. (2016). The Determinants of Industrialization: Empirical Evidence for Africa. European Scientific Journal, 219-239.##Samsami, H. and Amirjan, R. (2011). The Effect of Banking Facilities on the Value-Added of the Industry and Mining Sector in Iran. Journal of Economic Research and Policies. 19(59), 129- 150.##Samsami, H., Davoodi, P. and Amiri Javid, H. (2016). Comparing Effectiveness of Liquidity Growth on GDP, Private Investment and Employment with Assets Market Bubble, Journal of Economic Research, 51(2), 457-493.##Sarmad, A. A. (2017). A Comparative Study of Artificial Neural Networks and Adaptive Nero- Fuzzy Inference System for Forecasting Daily Discharge of a Tigris River. International Journal of Applied Engineering Research, 12(9), 2006-2016.##Shahjahan, A., Khandaker, J. A. and Shafiul Islam, Md. (2016). Effects of Trade Openness and Industrial Value Added on Economic Growth in Bangladesh. International Journal of Sustainable Development Research, 2(3), 18-23.##Sözen, A. and Arcaklioğlu, E. (2007). Prospects for future projections of the basic energy sources in Turkey. Energy Sources, Part B, 2(2), 183-201.##Statistics Center of Iran, Data and Statistics website (www.amar.org.ir)##Tiwari, M. K., Bajpai, S. and Dewangan, U. K. (2012). Prediction of Industrial Solid Waste with ANFIS Model and its comparison with ANN Model- A Case Study of Durg-Bhilai Twin City India. International Journal of Engineering and Innovative Technology, 2(6), 192-201.##Van Eyden, R. J. (1996). The Application of Neural Networks in the Forecasting of Share Prices. Finance and Technology Publishing, Haymarket, VA.##Wacziarg, R. (2000). Measuring the Dynamic Gains from Trade. The World Bank Economic Review, 15(34), 55-78.##World Bank website (www.worldbank.org)##Zamanzadeh, H. (2010). A decade of Iran's economy performance in terms of macroeconomic indicators. Tazehaye Eghtesad, 8(129), 35-43.##Zarra Nejad, M. and Raoofi, A. (2015). Evaluation and Comparison of Forecast Performance of Linear and Non-linear Methods for Daily Returns of Tehran Stock Exchange. Financial Monetary Economics Research, 22(9), 1-28.##

1 تأثیر سیاست پولی بر متغیرهای کلان اقتصادی با فرض محدودیت وثیقه The Impacts of Monetary Policy on Macroeconomic Variables Assuming the Collateral Constraint https://ijes.shirazu.ac.ir/article_5209.html 10.22099/ijes.2019.31442.1508 1 This study aimed to examine the effects of monetary policy on macroeconomic variables with regard to the collateral constraint. For this purpose, a dynamic stochastic general equilibrium (DSGE) was developed for Iran’s economic status. Two scenarios were considered as to account for the behavior of the central bank. In the first scenario, the monetary rule is modeled according to the GDP gap and inflation. In the second scenario that is modeled by macro-prudential rule, in addition to the GDP gap and inflation, it is also the central bank responses to the housing price gap that contributes to a steady state. An examination of the impulse response functions in the two scenarios indicated that the monetary shock increased production and inflation. A monetary shock has a positive impact on the consumption of patient households (lenders) and a negative effect on impatient households’ (borrowers) consumption. The collateral constraint was assumed to cause the effects of shocks to be continued on both groups. A comparison between the two scenarios indicated that if the central bank responds to the housing price deviation, in addition to the GDP gap and inflation, the effectiveness of the monetary policy will be strengthened. 0 هدف این مقاله بررسی اثر سیاست‌ پولی بر تولید و تورم در شرایط محدودیت وثیقه است. به این منظور یک الگوی تعادل عمومی پویای تصادفی (DSGE) برای شرایط اقتصاد ایران طراحی گردیده است. برای رفتار بانک مرکزی دو سناریو در نظر گرفته‌شده است. در سناریو اول شوک درآمد نفت و شوک مخارج دولت بر نرخ رشد پایه پولی اثر می‌گذارد. در سناریو دوم که بر اساس قاعده کلان احتیاطی طراحی‌شده، قاعده پولی بر اساس شکاف تولید، تورم و قیمت مسکن از مقادیر وضعیت پایدار مدل‌سازی گردیده است. بررسی توابع عکس‌العمل آنی نشان می‌دهد شوک پولی، تولید و تورم را افزایش می‌دهد. اثر شوک پولی بر مصرف خانوارهای صبور (دهنده) مثبت و برای خانوارهای کم‌طاقت (قرض گیرنده) منفی است. همچنین مقایسه روند تولید ناخالص داخلی و داده‌های شبیه‌سازی‌شده نشان می‌دهد که سناریو اول بهتر از سناریو دوم (قاعده کلان احتیاطی) می‌تواند واقعیت‌های اقتصاد ایران را توضیح دهد. 69 90 هادی کشاورز Hadi Keshavarz Department of economics, Persian gulf university, bushehr , Iran Iran hd.keshavarz@gmail.com حجت پارسا Hojat Parsa Department of Economics, Persian Gulf University, Bushehr, Iran Iran hojat_parsa@yahoo.com سیاست پولی محدودیت وثیقه قاعده کلان احتیاطی الگوی تعادل عمومی پویای تصادفی ایران References##Abolhassani A., Ebrahimi I., Hossein Pour M.,&  Bahrami Nia  E. (2016). The Effect of Oil Shocks and Monetary Shocks on Production and Inflation in the Housing Sector of the Iranian Economy: New Keynesian Dynamic Stochastic General Equilibrium Approach. Quarterly Journal of Economic Growth and Development Research, 7(25), 97-112 (in Persian).##Badarau, C., & Popescu, A., (2014). Monetary policy and credit cycles: a DSGE analysis. Economic Modelling, 42, 301–312.##Bayat M., Afshari Z., & Tavakolian H. (2016). Monetary Policy and Stock Price Index (on the basis of the wealth effect of the stock market boom) in a DSGE Framework. Applied economics studies in Iran (AESI), 5(20), 33-61(in Persian).##Bernanke, B. S., & Gertler, M. (1989). Agency costs, net worth, and business fluctuations, American Economic Review, 79(1), 14-31.##Borio, C., & Zhu, H. (2012). Capital regulation, risk-taking and monetary policy: a missing link in the transmission mechanism?. Journal of Financial Stability, 8 (4), 236–251.##Carlstrom, C. T., & Fuerst T. S. (1997). Agency costs, net worth, and business fluctuations: A computable general equilibrium analysis. American Economic Review, 87(5), 893-910.##De Grauwe, P., & Gros, D. (2009). A new two-pillar strategy for the ECB. CESifo Working Paper, no 2818.##Ehsani M. A., Keshavarz h., & Keshavarz M. (2017). The Impact of Monetary and Fiscal Policies on Employment Fluctuations with an Emphasis on Private Sector Employment. Quarterly Journal of Economic Growth and Development Research, 7(26), 124-113##Erfani A., & Shamsiyan S. (2016). Application of Taylor's Rule in Iran Economy and Policies influence from Real Estate Market. Journal of investment knowledge, 5(18), 197-210.##Faia, E., & Monacelli, T. (2007). Optimal interest rate rules, asset prices, and credit frictions. Journal of Economic Dynamics and Control, 31, 3228–3254.##Fakhrehosseini S. F., Shahmoradi A., & Ehsani M. A. (2012). Sticky Prices, Wages and Monetary Policy in the Iranian Economy. The Economic Research, 12(1), 1-30 (In Persian).##Faraji M., Afshari Z., & Ebrahimi I. (2015).Oil Price Shocks and Monetary Policy in Iran: The New Keynesian Approach. Journal of Monetary & Banking Research, 7(22), 533-568(in Persian).##Fotros, M. H., Tavakolian, H., & Maaboudi, R. (2015). Impact of Fiscal and Monetary Shocks on Macroeconomic Variables in Iran, Dynamic Stochastic General Equilibrium Approach 1961-2012. Quarterly Journal of Economic Growth and Development Research, 5(19), 73-94 (in Persian).##Heidari, H., & Molabahrami, A. (2017). The impact of credit shocks on dynamics of financial and macroeconomic variables using a DSGE model for the Iran economy. Journal of Economic Research and Policies, 24(80), 85-118 (in Persian).##Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle. American Economic Review, (95)3, 739-764.##Iacoviello, M., & S. Neri. )2010(. Housing Market Spillovers: Evidence from an Estimated DSGE Model. American Economic Journal: Macroeconomics, 2 (2): 125–64.##Jafari Samimi, A., Tehranghian A. M., Ebrahimi I., & Balonezhad Noori R. (2014). The Effect of Monetary and Non-Monetary Shocks on Inflation and Output in Dynamic Stochastic General Equilibrium Model in Open Economy Condition: Case Study of Iran Economy. Applied economics studies in iran(AESI), 3(10), 1-32 (in Persian).##Kannan, P., Rabanal, P., & Scott, A. (2012). Monetary and macroprudential policy rules with house price booms. The B.E. Journal of Macroeconomics, 12(1), 1- 44.##Kiyotaki, N., & Moore J. (1997). Credit cycles. Journal of Political Economy, 105(2), 211-248.##Komijani, A., & Tavakolian, H. (2012). Monetary policy under fiscal dominance and implicit inflation target in Iran: A DSGE approach. Journal of Economic Modeling Research, 2(8), 87-117. (in Persian)##Manzoor, D. & Taghipour A. (2016). A dynamic stochastic general equilibrium model for an oil exporting and small open economy: the case of Iran. Journal of Economic Research and Policies, 23(75), 7-44. (in Persian)##Monacelli, T. )2009(. New Keynesian Models, Durable Goods, and Collateral Constraints. Journal of Monetary Economics, 56 (2):242–54.##Sahabi, B., Asgharpur H., & Qorbani S) .2017(. The Monetary Shocks' Asymmetry within Dynamic Stochastic General Equilibrium Model. The Economic Research, 17(2), 73-100 (in Persian).##Semmler, W., & Zhang, W. (2007). Asset price volatility and monetary policy rules: a dynamic model and empirical evidence. Economic Modelling, 24, 411–430.##Shahmoradi, A., & Ebrahimi E. (2010).The Impacts of Monetary Policies in Iran: A DSGE Approach. Journal of Monetary and Banking Researches, 2(3).31-56 (in Persian).##Townsend, R. M. (1979). Optimal Contracts and Competitive Markets with Costly State Verification. Journal of Economic Theory, 21(2), 265-293.##

1 مدل سازی و مقایسه شبکه های عصبی مصنوعی GMDH و RBF در پیش بینی مصرف فرآورده های نفتی در بخش کشاورزی The Modeling and Comparison of GMDH and RBF Artificial Neural Networks in Forecasting Consumption of Petroleum Products in the Agricultural Sector https://ijes.shirazu.ac.ir/article_5021.html 10.22099/ijes.2018.29049.1444 1 Energy plays a significant role in today's developing societies. The role of energy demands to make decisions and policy with regard to its production, distribution, and supply. The vital importance of energy, especially fossil fuels, is a factor affecting agricultural production. This factor has a great influence on the production of agricultural products in Iran. The forecast of the consumption of oil products by the agricultural sector can help managers and planners to adopt sound management practices for their consumption. Presently, artificial neural networks are regarded as a powerful tool for the analysis and modeling of nonlinear relationships. The present study employed GMDH and RBF artificial neural networks to estimate the consumption of oil products by the agricultural sector. The underpinning parameters were selected to include the value added to the fixed price, rural population, agricultural land area, agricultural mechanization (tractor), and the consumption rate of oil products, electricity, price of oil products, and total energy use by the agricultural sector for the period of 1967-2017. The comparison of MSE, MAE, and MAPE for the GMDH and RBF models showed that the GMDH neural network was highly capable of modeling the energy consumption of the agricultural sector. 0 انرژی در جوامع در حال توسعه نقش مهمی دارد. نقش تقاضای انرژی در تصمیم گیری و سیاست گذاری بر تولید، توزیع و عرضه آن و اهمیت حیاتی انرژی، به ویژه سوخت های فسیلی، یک عامل موثر بر تولید کشاورزی است. این عامل تأثیر زیادی بر تولید محصولات کشاورزی در ایران دارد. پیش بینی مصرف محصولات نفتی توسط بخش کشاورزی می تواند به مدیران و برنامه ریزان کمک کند تا شیوه های مدیریت مناسب برای مصرف خود را به کار گیرند. در حال حاضر شبکه های عصبی مصنوعی به عنوان یک ابزار قدرتمند برای تحلیل و مدل سازی روابط غیر خطی در نظر گرفته می شوند. در این تحقیق، شبکه های عصبی مصنوعی GMDH و RBF به منظور تخمین مصرف محصولات نفتی توسط بخش کشاورزی مورد استفاده قرار گرفت. پارامترهای پایه ای شامل ارزش افزوده به قیمت ثابت، جمعیت روستایی، مساحت زمین های کشاورزی، مکانیزاسیون کشاورزی (تراکتور) و میزان مصرف محصولات نفتی، برق، قیمت محصولات نفتی و مصرف انرژی کل کشاورزی بخش برای دوره 1967-2017 انتخاب شدند. مقایسه MSE، MAE و MAPE برای مدلهای GMDH و RBF نشان داد که شبکه عصبی GMDH توانایی بالایی در مدل کردن مصرف انرژی بخش کشاورزی دارد. 91 105 مجتبی عباسیان Mojtaba Abbasian Faculty of Economic, Chabahar University, Chabahar, Iran Iran abbasian@cmu.ac.ir علی سردار شهرکی Ali Sardar Shahraki Department of Economic, University of Sistsn and Baluchestan, Zahedan,Iran Iran a.shahraki65@gmail.com جواد شهرکی Javad shahraki Department of Economic, University of Sistsn and Baluchestan, Zahedan,Iran Iran j.shahraki@eco.usb.ac.ir شبکه های عصبی مصنوعی نفت عملکرد پایه شعاعی (RBF) GMDH بخش کشاورزی References##Abbasi, E. (2015). Prediction of energy consumption by the agricultural sector of Iran. Quarterly Journal of Financial Economics, 9 (32), 81-102. (In Persian)##Alam, M. S., Alam, M. R. & Islam, K. K. (2005). Energy flow in agriculture: Bangladesh, American Journal of Environmental Science, vol. 3, 213-220.##Atashkari, K., Nariman-Zadeh, N., Gölcü, M., Khalkhali, A. & Jamali, A. (2007) Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms; Energy Conversion and Management, Vol. 48, Issue 3, 1029-1041.##Barthelmie, R. J., Murray, F., & Pryor, S. C. (2008). The economic benefit of short-term forecasting for wind energy in the UK electricity market. Energy Policy, 36(5), 1687-1696.##Ebrahimi, M. (2012). Use of artificial neural network (ANN) and time series approach for prediction of electricity consumption in agricultural sector. Journal of Agricultural Economics Research, 4 (13), 27-42. (In Persian with English Abstract)##Ghasemi, A. (2012). An overview of the evolution of energy economy indicators in the agricultural sector, the monthly review of issues and economic policies, 3: 169-184. (In Persian)##Haykin, S. (1999). Neural networks: a comprehensive foundation, Prentice Hall, New Jersey, USA.##Ivakhnenko, A. G., & Muller J. A. (1995). Present state and new problems of further GMDH development,System Analysis Modeling and Simulation; (SAMS), Vol. 20, No. 1-2, 3-16.##Ivakhnenko, A. G., & Müller, J. A. (1995). Recent Developments of Self-Organising Modeling in Prediction and Analysis of Stock Market, < https://pdfs.semanticscholar.org/be26/7e3a9c2843cd756a4ef029a295104225ae0c.pdf >##Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438.##Lee, Y. S., & Tong, L. I. (2012). Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Applied Energy, 94, 251-256.##Li, K., & Su, H. (2010). Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system. Energy and buildings, 42(11), 2070-2076.##Menhaj, M., Kazemi, A., Shakuri Ghanjwari, H., Mehrgan, M., & Taghizadeh, M. (2010). Transport energy demand forecasting using neural networks: Case study Iran. Management Research in Iran, 14 (2), 203-220. (In Persian with English Abstract)##Mousavi, S., Mokhtari, Z., & Farajpour, Z. (2010). Prediction of energy carriers consumption rate by the agricultural sector of Iran: The application of ARCH and ARIMA models. Quarterly Journal of Energy Economics Review, 7 (27), 181-195. (In Persian)##Nelles, O. (2001). Nonlinear system identification, Springer Verlag, Berlin.##Pukšec, T., Krajačić, G., Lulić, Z., Mathiesen, B. V., & Duić, N. (2013). Forecasting long-term energy demand of Croatian transport sector. Energy, 57, 169-176.##Sadeghi, H., Afzalian, A., Haghani, M., &Sohrabivafa, H. (2013). Forecasting the long run electricity demand using hybrid PSO-ANFIS algorithm. Journal of Economic Modeling Research, 3 (10), 21-56. (In Persian with English Abstract)##Sardar Shahraki, A. (2017). Optimal allocation of Hearmand's water resources resources using game theory and evaluation of management scenarios. PhD Thesis, Agricultural Economics University of Sistan and Baluchestan, Zahedan, Iran##Taghizadeh Mehrjerdi, R., Fatahi Ardakani, A., Tahari, M.H., & Babaie, H. (2015). Prediction of Iran's agricultural energy consumption using the combined model of genetic algorithm and artificial neural networks, Agricultural Economics Research, 3: 149-166. (In Persian)##Taheri, F., & Mousavi, S. (2010). Analyzing the role of energy in the Iranian agricultural sector. Journal of Agricultural Economics Research, 2 (6), 45-60. (In Persian with English Abstract)##Wang, X.,Li, K.,Li, H.,Bai, D., & Liu, J. (2017). Research on China’s rural household energy consumption–Household investigation of typical counties in 8 economic zones. Renewable and Sustainable Energy Reviews, 68, 28-32.##

1 عوامل تعیین کننده انتخاب شیوه های حمل و نقل غیررسمی در شهر نورث سنترال ایلورین نیجریه The Determinants of the Mode of Informal Transport Chosen in North Central City of Ilorin, Nigeria https://ijes.shirazu.ac.ir/article_5269.html 10.22099/ijes.2019.31386.1506 1 Transportation is a necessity if the day-to-day economic activities of the society must move on. There are different modes of informal transport available in Nigeria. This study evaluated the determinants of the modal choice of informal transport among commuters in North central city of Ilorin, Nigeria. The study used primary data generated through a structured questionnaire administered to 100 commuters randomly selected in Ilorin metropolis. The study used a multinomial logit model for data analysis. The results showed that earnings and household size were the core determinants of the choice of informal transport mode. The study concluded that different informal transport modes available to the commuters gave them the opportunity to make choices based on their income and the transport cost in Ilorin metropolis, Nigeria. 0 - 107 118 احمد یاکوبو Ahmed Yakubu Department of Economics, Faculty of Social Sciences, University of Ilorin, Nigeria Nigeria taruwere@gmail.com محمد آدیبایو اوجولپ Mohammed Adebayo Ojuolape Department of Economics, Faculty of Social Sciences, University of Ilorin, Nigeria Nigeria ojuolape.ma@unilorin.edu.ng جمیل ثانی Jemeel Sanni Department of Economics, University of Ilorin, Kwara State, Nigeria Nigeria sannijemeel@gmail.com حمل و نقل غیر رسمی انتخاب نیجریه مدل چند منظوره لوگیت References##Aderamo, A. J., (2010). Transport in Nigeria: The case of Kwara State. AfricanEconomic and Business Review,8(1), 19-40.##Adeyemo, M. A., (1998). An appraisal of motorcycle as a commercial passenger transport mode in Port Harcourt metropolis. Journal of Transport Studies, 2(1), 77-89.##Aditjandra, P. T., Cao, X. and Mulley. C., (2012). Understanding neighborhood design impact on travel behavior: An application of structural equations model to a British metropolitan data. Transportation Research Part A: Policy and Practice 46(1): 22–32. doi:10.1016/j.tra.2011.09.001.##Aditjandra, P. T., Cao, X. and Mulley. C.,  (2016). Exploring changes in public transport use and walking following residential relocation: A British case study.##Ahmed, Y. A., (2009). Settlements pattern and functional distribution in an emerging community: A case study of a local government area of kwara state, Nigeria. Medwell J. the Social Sciences 4(3): 256-263.##Akogun, I. T., and Ojo, O., (2013). Tenant eviction in property management practice in ilorin metropolis, Nigeria. Journal of Economics and international finance, 5(4), 139-145.##Arosanyin, G.T., and Yakubu, A. T., (2014). Driver License Compliance Among Commercial Motorcyclists in Kwara State, Nigeria: In Road Logistics in Support of Development: Selected Papers from the 5th Regional Conference for Africa of the South African Road Federation/ International Road Federation held in Pretoria South Africa between 2-4 September 2014, ISBN 978-0-620-61999-8; Chapter 5, Folder 7, File no. 3. Published by SARF/IRF. Available at www.sarf.org.za/uploads/.##Chee,   W. L., and Fernandez, J. L.,  (2013). Factors that Influence the Choice of Mode of Transport in Penang: A Preliminary Analysis.   Procedia - Social and Behavioral Sciences 91 (2013) 120 – 127.##Danmole, H. O., (1980). The Frontier Emirate: A History of Islam in Ilorin. An unpublishedPh.D Thesis.##Handy, S., Cao, X., and Mokhtarian. P., (2005). Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research PartD:Transport and Environment 10(6): 427–444. doi:10.1016/j.trd.2005.05.002.##Kamargianni, M., Dubey, S., Polydoropoulou, A., and Bhat C., (2015).  Investigating the Subjective and Objective Factors Influencing Teenagers' School TravelModeChoice – An Integrated Choice and Latent Variable Model.  m.kamargianni@ucl.ac.uk##Li, M., Song, G., Cheng, Y., and Yu, L., (2015). Identification of Prior Factors Influencing the Mode Choice of Short Distance Travel. Discrete Dynamics in Nature and Society, Volume 2015, Article ID 795176, http://dx.doi.org/10.1155/2015/795176.##Maddala, G. S., (1983). Limited-dependent and qualitative variables in econometrics.Cambridge: Cambridge University Press.##National Bureau of Statistics (NBS), (2006). Federal Republic of Nigeria, 2006 Population Census. Retrieved August 1, 2009, from http://www.nigerianstat.gov.ng/Connections/Pop2006.pdf.##Ogunsanya, A., and Galtima, M., (1993). Motorcycle in public passenger transport service in Nigeria: A case study of Yola town, urban passenger transportation in Nigeria., Lagos: Heinemann Books.##Ojekunle, J. A., (1998). Operations and use of motorcycles as a mode public passenger transport. In Dange, H., Chikolo, I.V., and Ogunsanya, A. A. (eds). Issues in transport planning and management (pp.84-103). Zaria. NITT.##Oyesiku, K. O., (2001). City poverty and emerging mobility crisis: The use of motorcycle as public transport in Nigerian cities. The proceedings of the 9thworld conference of transport research, held in Seoul, Japan, 22-27 July, 2001. 65-70.##Scheiner, J., and Holz-Rau. C., (2007). Travel mode choice: Affected by objective or subjective determinants? Transportation 34(4): 487–511. doi:10.1007/s11116-007-9112-1.##Williams, H. G., (2008). Econometric analysis. Pearson publications USA. Sixth edition##Yakubu, A. T., (2015). Policy Issues Relating to Informal Transport in Developing World:In Policy Issues and Development;pp 173-189. H.M. Bandara (ed.). Published by Stanford Lake (Pvt) Ltd Pannipitiya Sri Lanka.##

1 تجزیه و تحلیل تجربی از استراتژی رقابت و نرخ عبور ارز An Empirical Analysis of Strategic Competition and Exchange Rate Pass-Through https://ijes.shirazu.ac.ir/article_5315.html 10.22099/ijes.2019.31929.1530 1 The aim of this article is to study the firm-level pricing behavior based on the firm’s competitive strategy through the exchange rate pass-through. Using Iranian export price microdata, we provide new empirical evidence on how firm’s exchange rate pass-through depends on firm’s strategic decisions of competition. After classifying firms in two groups based on their competitive strategies, we show that firms involving in strategic complements pass more exchange rate movements to export prices than firms with strategic substitutions. Furthermore, firms in strategic substitutions tend to increase their export volume significantly more than the firms in strategic complements as a result of the depreciation of exchange rate. 0 - 119 136 سعید راسخی Saeed Rasekhi Department of Economics, University of Mazandaran, Babolsar, Iran Iran srasekhi@umz.ac.ir زهرا شیدایی Zahra Sheidaei Department of Economics, University of Mazandaran, Babolsar, Iran Iran sheidaee_zahra@yahoo.com استراتژی رقابتی مکمل های استراتژیک عبور از نرخ ارز ایران References##Amirteimoori, S., & Chizari, A. (2010). An investigation of comparative advantage of pistachio production and exports in Iran. Journal of Agricultural Science and Technology, 10, 395-403.##Amiti, M., Itskhoki, O., & Konings, J. (2014). Importers, exporters, and exchange rate disconnect. American Economic Review, 104(7), 1942-1978.##Atkeson, A., & Burstein, A. (2007). Pricing-to-market in a Ricardian Model of International Trade. American Economic Review, 97(2), 362-367.##Auer, R. A., & Schoenle, R. S. (2016). Market structure and exchange rate pass-through. Journal of International Economics, 98, 60-77.##Barhoumi, K. (2006). Differences in long run exchange rate pass-through into import prices in developing countries: An empirical investigation. Economic Modelling, 23(6), 926-951.##Bergin, P. R., & Feenstra, R. C. (2009). Pass‐through of exchange rates and competition between floaters and fixers. Journal of Money, Credit and Banking, 41, 35-70.##Berman, N., Martin, P., & Mayer, T. (2012). How do different exporters react to exchange rate changes? The Quarterly Journal of Economics, 127(1), 437-492.##Bernard, A. B., Eaton, J., Jensen, J. B., & Kortum, S. (2003). Plants and productivity in international trade. American Economic Review, 93(4), 1268-1290.##Bernard, A. B., Jensen, J. B., Redding, S. J., & Schott, P. K. (2009). The margins of US trade. American Economic Review, 99(2), 487-493.##Brander, J., & Krugman, P. (1983). A ‘reciprocal dumping’model of international trade. Journal of international Economics, 15(3-4), 313-321.##Brander, J. A. (1981). Intra-industry trade in identical commodities. Journal of international Economics, 11(1), 1-14.##Brander, J. A., & Spencer, B. J. (2015). Intra-industry trade with Bertrand and Cournot oligopoly: The role of endogenous horizontal product differentiation. Research in Economics, 69(2), 157-165.##Bulow, J. I., & Pfleiderer, P. (1983). A note on the effect of cost changes on prices. Journal of Political Economy, 91(1), 182-185.##Chatterjee, A., Dix-Carneiro, R., & Vichyanond, J. (2013). Multi-product firms and exchange rate fluctuations. American Economic Journal: Economic Policy, 5(2), 77-110.##Coulibaly, D., & Kempf, H. (2010). Does inflation targeting decrease exchange rate pass-through in emerging countries?. Banque de France Working Paper No. 303.##Eaton, J., Kortum, S., & Kramarz, F. (2004). Dissecting trade: Firms, industries, and export destinations. American Economic Review, 94(2), 150-154.##Fisher, E. O. N. (1989). A model of exchange rate pass-through. Journal of international Economics, 26(1-2), 119.##Froeb, L., Tschantz, S., & Werden, G. J. (2005). Pass-through rates and the price effects of mergers. International Journal of Industrial Organization, 23(9-10), 703-715.##Garetto, S. (2016). Firms' heterogeneity, incomplete information, and pass-through. Journal of International Economics, 101, 168-179.##Goldberg, P. K., & Hellerstein, R. (2007). A framework for identifying the sources of local-currency price stability with an empirical application. In: National Bureau of Economic Research Cambridge, Mass., USA.##Goldberg, P. K., & Knetter, M. M. (1996). Goods prices and exchange rates: What have we learned?. NBER Working Paper No. 5862.##Gopinath, G., & Itskhoki, O. (2010). Frequency of price adjustment and pass-through. The Quarterly Journal of Economics, 125(2), 675-727.##Gopinath, G., Itskhoki, O., & Rigobon, R. (2010). Currency choice and exchange rate pass-through. American Economic Review, 100(1), 304-336.##Gopinath, G., & Rigobon, R. (2008). Sticky borders. The Quarterly Journal of Economics, 123(2), 531-575.##Hellerstein, R. (2008). Who bears the cost of a change in the exchange rate? Pass-through accounting for the case of beer. Journal of International Economics, 76(1), 14-32.##Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners and trading volumes. The Quarterly Journal of Economics, 123(2), 441-487.##Krugman, P. (1980). Scale economies, product differentiation, and the pattern of trade. The American Economic Review, 70(5), 950-959.##Krugman, P. R. (1979). Increasing returns, monopolistic competition, and international trade. Journal of international Economics, 9(4), 469-479.##Lyandres, E. (2006). Capital structure and interaction among firms in output markets: Theory and evidence. The Journal of Business, 79(5), 2381-2421.##Martin, L. M., & Rodriguez, D. R. (2004). Pricing to market at firm level. Review of World Economics, 140(2), 302-320.##Melitz, M. J. (2003). The impact of trade on intra‐industry reallocations and aggregate industry productivity. Econometrica, 71(6), 1695-1725.##Melitz, M. J., & Ottaviano, G. I. (2008). Market size, trade, and productivity. The review of economic studies, 75(1), 295-316.##Menon, J. (1996). The degree and determinants of exchange rate pass-through: market structure, non-tariff barriers and multinational corporations. The Economic Journal, 434-444.##Nakamura, E., & Zerom, D. (2010). Accounting for incomplete pass-through. The review of economic studies, 77(3), 1192-1230.##Rasekhi, S., & Sheidaei, Z. (2018). Tariff Pass-through and Firm’s Productivity: A Case Study of Iran. Iranian Economic Review. DOI: 10.22059/IER.2018.68954##Rodriguez-Lopez, J. A. (2011). Prices and exchange rates: A theory of disconnect. The Review of Economic studies, 78(3), 1135-1177.##Shaked, A., & Sutton, J. (1982). Natural oligopolies and international trade: an introduction. STICERD, Theoretical Economics Paper Series 51, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.##Sundaram, A. K., John, T. A., & John, K. (1996). An empirical analysis of strategic competition and firm values the case of R&D competition. Journal of Financial Economics, 40(3), 459-486.##Ten Kate, A., & Niels, G. (2005). To what extent are cost savings passed on to consumers? An oligopoly approach. European Journal of Law and Economics, 20(3), 323-337.##Zare Mehrjerdi, M., & Tohidi, A. (2014). An Empirical Analysis of Exchange Rate Pass-Through to Iran's Saffron Export Price. Ethno-Pharmaceutical Products, 1(1), 29-36.##Zhou, D., Spencer, B. J., & Vertinsky, I. (2002). Strategic trade policy with endogenous choice of quality and asymmetric costs. Journal of international Economics, 56(1), 205-232.##Zimmerman, P. R., & Carlson, J. A. (2010). Competition and cost pass-through in differentiated oligopolies. MPRA_paper_25931.##

1 عوامل حیاتی موفقیت گذار به اقتصاد یادگیرنده در کشورهای در حال توسعه: مطالعه موردی ایران با رویکرد تئوری داده بنیاد The Success Factors of Developing Countries' Transition to a Learning Economy: Evidences from Iran by a Grounded Theory Approach https://ijes.shirazu.ac.ir/article_5333.html 10.22099/ijes.2019.31565.1512 1 An outstanding feature of the contemporary world is the rapid economic, technological, social, and political changes marked by a high level of uncertainty. For surviving in this complex and constantly changing economy, successful transition to a learning economy is a necessity for developing countries. This research was aimed to investigate the factors which played a role in the developing countries’ successful transition to a learning economy. Furthermore, according to evolutionary economics, countries are path-dependent, i.e. the differences in structures and institutions of an economy give each economic system its specific nature that is illustrated in the particular challenges each country face in its transformation to a learning economy. Hence, based on the pieces of evidence from Iran, this inductive, exploratory, and qualitative research, using a grounded theory approach and a follow-up quantitative analysis based on survey data, led to the development of a model that can be used to analyze the success factors which contribute to this transition. The findings showed that in terms of the ‘paradigm model’, transitional thinking as casual condition, ICT, social capital and macro-economic conditions as intervening conditions, policy institution as central category, government, university and industry interactions, learning firms, collaborative learning, improved research and education system, and regional development as strategies were factors that could lead to a learning economy. 0 زندگی در دنیای امروز یا تغییرات سریع در حوزه های مختلف اقتصادی، فناورانه، اجتماعی و سیاسی همراه است که با میزان قابل توجهی از عدم قطعیت مشخص می شود. تکامل کشورهای در حال توسعه در این اقتصاد جهانی که با سرعت و پیچیدگی فراوان در حال تغییر است، نیازمند گذار موفق به اقتصاد یادگیرنده است. پژوهش حاضر به دنبال پاسخ به این سوال است که گذار موفق کشورهای در حال توسعه به اقتصاد یادگیرنده مستلزم توجه به چه عواملی است؟ از طرف دیگر، برابر اصل وابستگی به مسیر مطرح در اقتصاد تکاملی، کشورهای مختلف با اقتصادهای ملی، ساختارها، نهادها، ارزشها فرهنگها و تاریخچه متفاوت، شخصیت متمایز سیستم ملی خود را دارند که چالشهای مختص هر یک را برای گذار به اقتصاد یادگیرنده ایجاد می کند. بر این اساس، پژوهش کیفی، استقرایی و اکتشافی حاضر با رویکرد ترکیبی (کیفی و کمی) شامل تئوری داده بنیاد در مرحله کیفی و تحلیل کمی پیمایشی به توسعه مدلی می پردازد که برا تعیین عوامل موثر بر گذار ایران به اقتصاد یادگیرنده موثرند. یافته ها نشان می دهند که تفکر گذار، نهاد سیاست، فناوری اطلاعات و ارتباطات، سرمایه اجتماعی، شرایط اقتصاد کلان، توسعه منطقه ای، بنگاه های یادگیرنده، تعامل دولت، صنعت و دانشگاه، سیستم ارتقاء یافته آموزش و پژوهش و یادگیری همکارانه مبتنی بر اعتماد متقابل در این رابطه قابل توجه هستند. این عوامل در قالب مدلی بر اساس مدل پارادایم سازماندهی و ارایه شده اند. 137 161 بهنام عبدی Behnam Abdi Faculty of Management, Imam Ali Officer University, Tehran, Iran. Iran abdi220@gmail.com سید حمید خدادادحسینی Seyyed Hamid Khodadad Hosseini Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran. Iran khodadad@modares.ac.ir اقتصاد یادگیرنده اقتصاد دانش بنیان گذار نهاد سیاست تئوری داده بنیاد References##Abbasi, F., Hajihoseini, H., Mohammadi, M., & Elyasi, M. (2011). Analysis of Iranian innovation system's governance based on innovation policy making cycle. Journal of Science and Technology Policy, 4 (1) 33-49.##Amavilaha, V., Simplice, A.A., & Andrésd, A.R. (2017). Effects of globalization on peace and stability: Implications for governance and the knowledge economy of African countries. Technological Forecasting and Social Change, 122, 91-103.##Andria, D., & Savin, I. (2018). A Win-Win-Win? Motivating innovation in a knowledge economy with tax incentives. Technological Forecasting and Social Change, 127, 38-56.##Asongu, S.A. &  Andres, A.R. (2019). Trajectories of knowledge economy in SSA and MENA countries. Technology in Society, DOI: 10.1016/j.techsoc.2019.03.002 (March, 2019).##Carayannis, E.G.,  Ferreira, J.J.M.,  Jalali, M.S., & Ferreira, F.A.F. (2018). MCDA in knowledge-based economies: Methodological developments and real world applications. Technological Forecasting and Social Change, 131, 1-3.##Cavusoglu, B. (2016). Knowledge economy and north cyprus, Procedia Economics and Finance, 39, 720-724.##Charmaz, K. (2000). Grounded theory methodology: Objectivist and constructivist qualitative methods. 509-535 in Handbook of Qualitative Research, 2nd Edition, edited by N. K. Denzin and Y.S. Lincoln. Thousand Oaks, CA: Sage.##Chen, m.h., & Zhang, G.P. (2010). Tacit knowledge acquisition and sharing in intra-organization.  Third International Symposium on Knowledge Acquisition and Modeling.##Choung, J., Hwang, H., & Song, W. (2014). Transitions of innovation activities in latecomer countries: An exploratory case study of South Korea. World Development, 54, 156–167.##Creswell, J.W. (2004). Research design: qualitative & quantitative approaches. California: SAGE publications.##Fitjar, R.D., & Rodríguez-Pose, A. (2013). Firm collaboration and modes of innovation in Norway. Research Policy, 42, 128– 138.##Freeman, C., & Perez, C. (1988). Structural crises of adjustment, Business cycles and investment behavior, 38-66.##French, S. (2004). Innovation and Social Learning: Institutional Adaptation in an Era of Technological Change. Journal of Economic Geography, 4(2):219-220.##Fu, X., Pietrobelli, C., & Soete, L. (2011). The role of foreign technology and indigenous innovation in the emerging economies: Technological Change and Catching-up. World Development, 39(7), 1204–1212.##Glaser, B. (1992). Basics of grounded theory analysis. Mill Valley, CA: The Sociology Press.##Glaser, B., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.##Hudson, J., & Minea, A. (2013). Innovation, intellectual property rights, and economic development: A unified empirical investigation. World Development, 46, 66–78.##Jensen, M.B., Johnson, B., Lorenz, E., & Lundvall, B.A. (2007). Forms of knowledge and modes of innovation. Research Policy, 36(5) 680-693.##Lemola, T. (2002). Convergence of national science and technology policies: the case of Finland. Research Policy, 31, 1481–1490.##Lundvall, B.A. (1992). National systems of innovation: Towards a theory of innovation and interactive learning. London: Pinter Publishers.##Lundvall, B.A. (2008). The Danish model and the globalizing learning economy – Lessons for developing countries. Department of Business Studies, Aalborg University.##Lundvall, B.A., Intarakumnerd, P., & Vang, J. (2006). Asia’s innovation systems in transition. Edward Elgar Publishing, Inc.##Lundvall, B.A., & Johnson, B. (1994). The learning economy. Journal of Industry Studies, 1(2), 23-42.##Lundvall, B.A., Rasmussen, P., & Lorenz, E. (2008). Education in the learning economy: A European perspective. Policy Futures in Education, 6 (6).##Mejri, K., MacVaugh, J.A., & Tsagdis, D. (2018). Knowledge configurations of small and medium-sized knowledge-intensive firms in a developing economy: A knowledge-based view of business-to-business internationalization, Industrial Marketing Management, 71, 160-170.##Mu, J., Tang, F., & Mac Lachlan, D.L. (2010). Absorptive and disseminative capacity: Knowledge transfer in intra-organization networks. Expert Systems with Applications, 37, 31–38.##OECD (2000). Knowledge Management in the Learning Society. Paris: OECD.##Ogundeinde, A., & Ejohwomu, O. (2016). Knowledge economy: A panacea for sustainable development in Nigeria. Procedia Engineering, 145, 790-795.##Okada, A. (2004). Skills development and inter-firm learning linkages under globalization: Lessons from the Indian automobile industry. World Development, 32 (7), 1265–1288.##Pieroni, M.P.P., McAloone, T.C. &  Pigosso, D.C.A. (2019). Business model innovation for circular economy and sustainability: A review of approaches. Journal of Cleaner Production, 215, 198-216.##Regional Information Center for Science and Technology, (2012). Available online at http://www.srlst.com,##Soskice, D. (1999). Divergent production regimes: Coordinated and uncoordinated market economies in the 1980s and 1990s in H. Kitschelt et al. (eds.), Continuity and change in contemporary capitalism. Cambridge: Cambridge university press, 101–34.##Stankovic, N. & Micic, Z. (2018). Innovating and management of the knowledge base on the example of IT applications. Telematics and Informatics, 35(5).##Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. London: Sage.##Strauss, A., & Corbin, J. (1998).  Basics of qualitative research: Techniques and procedures for developing grounded theory. London: Sage, 2nd edition.##Supreme Cultural Revolution Council (2003). The council for scientific and cultural monitoring and evaluation, Science and technology evaluation in Islamic Republic of Iran (1st macro evaluation). Tehran: Supreme Cultural Revolution Council publications.##World Bank (1998). World development report 1998/1999: Knowledge for development. World Bank publications: Washington, DC.##World Bank (2012). Knowledge appraisal measurement. World Bank publications: Washington D.C.##Xao, X., & Guan, J. (2009). A scale-independent analysis of the performance of the Chinese innovation system. Journal of Informatics, 3(4):321-331.##Zandiatashbar, A., & Hamidi, S. (2018). Impacts of transit and walking amenities on robust local knowledge economy. Cities, 81, 161-171.##

1 شکست بازار بین بانکی و اثر مقررات بال 3 در یک مدل DSGE در ایران Interbank Market Failure and the Effects of the Basel III Regulations in a DSGE Model for Iran https://ijes.shirazu.ac.ir/article_5513.html 10.22099/ijes.2019.33834.1577 1 In order to facilitate transactions among banks, the interbank market has been established in Iran since 2008. The primary objective of this market is to eliminate banking system liquidity deficiencies at a rate chosen by the Central bank of Iran. The importance of this rate is that it affects market interest rates through its effects on banks’ balance sheets. Banks’ balance sheets are also influenced by banking regulation, such as Basel regulations; thus, this study was aimed to investigate the effects of the interbank market in Iran by imposing Basel III regulations on the banking system. For this purpose, a dynamic stochastic general equilibrium model (DSGE) was designed that included the interbank market. The structural parameters of the designed model were estimated using the Bayesian method and the quarterly data on the period 2008-2015. Afterward, the effect of a positive interbank innovation on the economy’s dynamics was examined. The results showed that an increase in the interbank rate led to instability in the economy. It was concluded that an increase in the liquidity and capital adequacy requirement, as mentioned in the Basel III regulations, would reduce the negative effects of interbank shocks on macroeconomic variables and the economy would naturally become more stable. 0 به منظور تسهیل مبادلات بین بانک ها، بازار بین بانکی در سال 1387 در ایران تاسیس شد. هدف اصلی این بازار حذف کمبود نقدینگی سیستم بانکی در یک نرخ انتخابی بانک مرکزی ایران است. اهمیت این نرخ آن است که نرخ های بهره بازار را از طریق اثرگذاری بر ترازنامه بانک ها تحت تاثیر قرار می دهد. چون ترازنامه بانک ها همچنین تحت تاثیر قوانین بانکی از قبیل قوانین بال قرار می گیرد، در این مقاله اثرات بازار بین بانکی در ایران را با در نظر گرفتن مقررات بال 3 در سیستم بانکی بررسی می کنیم. برای این منظور، یک مدل تعادل عمومی پویای تصادفی (DSGE) که شامل بازار بین بانکی است، طراحی گردید. پارامترهای ساختاری مدل طراحی شده با به کار گیری روش بیزین و داده های فصلی طی دوره 1387 تا 1394 تخمین زده شد. سپس، اثر یک شوک مثبت بازار بین بانکی بر پویایی های اقتصاد بررسی گردید. نتایج نشان داد که افزایش در نرخ بهره بازار بین بانکی منجر به بی ثباتی در اقتصاد می شود. همچنین نتایج نشان می دهد که افزایش در ذخایر نقدینگی و کفایت سرمایه، همانطور که در مقررات بال 3 ذکر شده، اثرات منفی شوک بازار بین بانکی بر متغیرهای اقتصاد کلان را کاهش داده و بنابراین اقتصاد باثبات تر می شود. 163 183 مرضیه پیراحمدی Marzieh Pirahmadi Faculty of Social Sciences and Economics, AL Zahra University, Tehran, Iran. Iran pirahmadimarzieh@gmail.com زهرا افشاری Zahra Afshari Faculty of Social Sciences and Economics, AL Zahra University, Tehran, Iran. Iran z.afshari@alzahra.ac.ir مهدی صارم Mehdi Sarem Economic Research and Policies, Central Bank of the Islamic Republic of Iran, Tehran, Iran. Iran mehdi_sarem@yahoo.com بازار بین بانکی مقررات بال3 تعادل عمومی پویای تصادفی ذخایر نقدینگی ذخایر کفایت سرمایه References##Acharya, V.V., & Mora, N. (2015). A crisis of banks as liquidity providers. Journal of Finance, 70(1), 1-43.##Ahn, J., Bignon, V., Breton, R., & Martin, A. (2016). Interbank market and central bank policy, Federal Reserve Bank of New York Staff Reports, 763, 1-33.  ##Angelini, P., Clerc, L., Cúrdia, V., Gambacorta, L., Gerali, A., Locarno, A., Motto, R., Roeger, W., Van den Heuvel, S., & Vlček, J. (2011). Basel III: long-term impact on economic performance and fluctuations, Federal Reserve Bank of New York Staff Reports, 485.##Borghei, M., & Mohammadi, T. (2017). Effects of price stickiness on conditional exchange rate pass-through in Iran: a DSGE approach, Journal of Applied Economics Studies in Iran, 6(23), 85-115.##Bridges, J., Gregory, D., Nielsen, M., Pezzini, S., Radia, A., & Spaltro, M. (2014). The impact of capital requirements on bank lending, Bank of England Working paper, 486. 36 pages.##Brunnermeier, M. K. (2009). Deciphering the liquidity and credit crunch 2007-2008, Journal of Economic Perspectives, 23(1), 77-100.##Carrera, C., & Vega, H. (2012). Interbank market and macroprudential tools in a DSGE model, Working Paper series.##Dargahi, H., & Hadiyan, M. (2018). The effect of macroprudential policies of financial stability of Iran economy: DSGE approach. Journal of Monetary & Banking Research, 10(34), 559-590.##De Walque, G., Pierrard, O., & Rouabah, A. (2010). Financial (In) stability, supervision and liquidity injections: a dynamic general equilibrium approach. The Economic Journal,120(549), 1234-1261.##Dib, A. (2010). Banks, credit market frictions, and business cycles. Working Papers Bank of Canada, 10-24.##Edwards, S., & Vegh, C. A. (1997). Banks and macroeconomic disturbances under predetermined exchange rates. Journal of Monetary Economics, 40(2), 239–278.##Gerali, A., Neri, S., Sessa, L. & Signoretti, F. M. (2010). Credit and banking in a DSGE model of the Euro Area. Journal of Money, Credit and Banking, 42(1), 107– 141.##Gertler, M. & Karadi, P. (2011). A model of unconventional monetary policy, Journal of Monetary Economics, 58(1), 17-34.##Ghosh, R. D., Kohli, B., & Khatkale, S. (2013), Basel I to basel II to basel III: a risk management journey of Indian banks, AIMA Journal of Management & Research, 7, 2/4, 0974 – 497.##Giri, F. (2014). Does interbank market matter for business cycle fluctuation? An estimated DSGE model with financial frictions for the euro area, SSRN Electronic Journal, 1,1-58.##Giri, F. (2018). Does interbank market matter for business cycle fluctuation? An estimated DSGE model with financial frictions for the euro area, Economic Modeling, 1-13.##Goodhart, C., Sunirand, P., & Tsomocos, D. (2005). A Risk Assessment Model for Banks, Annals of Finance, 1(2), 197-224.##Harmanta, R. A., Oktiyanto, F., & Idham. (2014). Interbank market with DSGE Bank, Working Paper.##Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle, American Economic Review, 95(3), 739-764.##Iacoviello, M.& Neri, S. (2010). Housing market spillovers: evidence from an estimated DSGE model, American Economic Journal: Macroeconomics, 2(2), 125-64.##Kannan, P., Rabanal, P., & Scott, A. (2012). Monetary and macro-prudential policy rules in a model with house price booms, The B.E. Journal of Macroeconomics, Contributions, 12 (1), 1–44.##Majcher, P. (2015). Increased bank capital requirements: neither panacea nor poison, Procedia Economics and Finance, 25 ,249 – 255.##Mohebbi, S., Shahrestani, H., & Hojabr Kiani, K. (2017). Financial shocks and the role of monetary policy in Iran’s economy: interbank market in DSGE model, Journal of Economic Research and Policies, 25)81(, 123-153.##Motavaseli, M., Ebrahimi, I., Shahmoradi, A., & Komeijani, A. (2011). A new Keynesian dynamic stochastic general equilibrium(DSGE) model for an oil exporting country, JER, 10(4), 87-116.##Puri, M., Rocholl, J., & Steffen, S. (2011). Global retail lending in the aftermath of the US financial crisis: distinguishing between supply and demand effects. Journal of Financial Economics, 100, 556-578.##Rubio, M., & Carrasco-Gallego, J. (2016), Bank capital requirements and collateralised lending market, The Manchester School, 85, S1, 79–103.##Schuler, T., & Corrado, L. (2016). Interbank market failure and macro-prudential policies, Journal of Financial Stability, 1-52.##Shakdwipee, P., & Mehta, M. (2017). From Basel I to Basel II to Basel III, International Journal of New Technology and Research (IJNTR), 3, 1, 66-70.##Shakeri, A., & Ahmadian, A. (2014). A model of financial shocks at bank and interbank of Iran (DSGE), Journal of Money and Economy, 4, 19-58.##Taghipour, A., & Esfahanian, H. (2016). The analysis of business cycle of oil shocks and government expenditures and their mechanisms of influence on macroeconomic variables: a DSGE approach, Journal of Financial Economics, 10 (35), 75-102.##Tavakolian, H., & Komeijani, A. (2012). Monetary policy under fiscal dominance and implicit inflation target in Iran: a DSGE approach, Journal of Economic Modeling Research, 2(8), 87-117.##

1 پویایی فشار بازار ارز و تورم در ایران: رویکرد تغییر رژیم The Dynamics of Exchange Market Pressure and Inflation in Iran: Regime Switching Approach https://ijes.shirazu.ac.ir/article_5524.html 10.22099/ijes.2019.34098.1585 1 This study was an attempt to analyze the dynamic reaction of the exchange market pressure (EMP) to different states of the foreign exchange market and inflation in the Iranian economy during 1988:4-2017:4. To this end, the EMP index was calculated using Edwards’s (2002) and Kumah’s (2007) formulae. By considering inflation as the threshold variable and using Threshold Vector Autoregressive (TVAR) model, the results showed that lagged variables had no significant effects on EMP in a low inflation regime,  but inflation had significant effects on EMP in a high inflation regime. The results of using the Markov Switching Vector Autoregressive (MS-VAR) model showed that in EMP and INF equations, the autoregressive coefficients in all lags and in both regimes were significant; this emphasizes the stability of the estimated VAR model. Based on the results of the MS-VAR equations, the results of the Granger Causality Test showed that when the EMP switched to a high regime, the inflation would have a significant impact on the EMP, but in the regimes where the EMP was at a low level, the inflation was not the cause of the EMP. EMP in low inflation regimes could also affect inflation while EMP was not the cause of inflation in high inflation regimes. Therefore, the policymakers should note that increasing EMP, even in low inflation regimes, can lead to pressure on prices. On the other hand, an increase in the foreign reserves causes the EMP to switch to a high regime; then, the inflationary pressures at any level of the inflation rate can exacerbate the exchange market pressure, and policymakers would be unable to control the currency market. Thus, if the EMP is controlled, the effects of inflation on the EMP will be discontinued, and this is a key point for policymakers. 0 این مطالعه واکنش پویای فشار بازار ارز را به وضعیت‌های مختلف بازار ارز و تورم در اقتصاد ایران و طی دوره‌ی: 1396:04-1367:04 مورد تحلیل قرار می‌دهد. نتایج این مطالعه با در نظر گرفتن تورم به عنوان متغیر آستانه و با استفاده از مدل خودرگرسیون برداری آستانه‌ای (TVAR) نشان می‌دهد که در رژیم تورمی پایین متغیرهای باوقفه اثر معنی‌داری بر EMP ندارند اما در رژیم تورمی بالا، تورم اثر معنی‌داری برEMP دارد. همچنین، نتایج مطالعه با استفاده از مدل خودرگرسیون برداری چرخش مارکوف (MS-VAR) نشان می‌دهد که در معادلات تورم و EMP ضرایب خودرگرسیونی در تمام وقفه‌ها و در هر دو رژیم معنی‌دار هستند و این بر ثبات مدل برآورد شده تأکید می‌کند. نتایج آزمون علیت گرنجر بر پایه معادلات MS-VAR نشان می‌دهد زمانیکه EMP به رژیم بالا چرخش می‌کند تورم تأثیر معنی‌داری بر EMP دارد اما در رژیم‌هایی با سطح پایین EMP، تورم علت گرنجری EMP نخواهد بود. EMP در رژیم‌های تورمی پایین می‌تواند بر تورم اثرگذار باشد اگرچه EMP در رژیم‌های تورمی بالا علت گرنجری تورم نخواهد بود. بنابراین سیاستگذاران باید به این موضوع توجه کنند که افزایش EMP در رژیم‌های تورمی پایین نیز می‌تواند منجر به فشار بر قیمت‌ها شود. 185 206 سید یحیی ابطحی Sayed Yahya Abtahi Accounting and economic dep. islamic azad university, yazd.iran Iran abtahi@iauyazd.ac.ir الهام امراللهی بیوکی Elham Amrollahi Bioki Department of Economic , Science and Research branch, Islamic Azad University, Tehran, Iran Iran amrollahi_elham@yahoo.com فشار بازار ارز پویایی تورمی سیاست های پولی مدل مارکوف مدل آستانه ایران References##Baghjari, M.,  Hoseininasab,E., & Najarzadeh, R. (2014). The impact of monetary policy on exchange market pressure: The Case of Iran, Journal of Economic Research and Policies 53-78.##Bird, G.,& Mandilaras, A. (2006). Regional Heterogeneity in the Relationship between Fiscal Imbalances and Foreign Exchange Market Pressure, Journal of World Development 34(7): 1171-1181.##Chan, K. S., (1993). Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model, The Annals of Statistics, 21, 520-533. ##De Macedo, J., Pereira, L.,& Reis, A. (2009). Comparing Exchange Market Pressure across Five African Countries, Journal of Open Economy Review, 20: 645-682.##Dempster, A. P., Laird, N. M.,& Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39, 1-38.##Edwards, S. (2002). Does the Current Account Matter?, National Bureau of Economic Research 21-76.##Eichengreen, B. J., Rose, A.K.,& Wyplosz, C. (1994). Speculative Attacks on Pegged Exchange Rates: an Empirical Exploration with Special Reference to the European Monetary System, NBER Working Paper. No. 4898.##Eichengreen, B. J., Rose, A. K.,& Wyplosz, C. (1995). Exchange Market Mayhem: The Antecedents and Aftermath of Speculative Attacks, Economic Policy, 10 (21): 249-312.##Enders,W. (2010). Applied Economic Time Series, 3end Edition.##Fiadora, V., & Biekpe, N. (2015). Monetary policy and exchange market pressure evidence from sub-Saharan Africa, Applied Economics, 47:3921-3937.##Franco, F., Delgado, J., Monteiro, S.,& Silva, P. (2014). Exchange Rate Pressure in Angola, Nova Africa Center for Business and Economic Development Working Paper Series, N0. 1502.##Gallant, A., Ronald, P.,& Tauchen,G. (1993). Nonlinear Dynamic Structures. Econometrica, 61 (4):871–907.##Gilal, M. A. (2011). Exchange Market Pressure and Monetary Policy: A Case Study of Pakistan, University of Glasgow.##Gilal, M. A.,& Byrne, J. P. (2015). Foreign Exchange Market Pressure and Capital Controls, International Financial Markets Institutions and Money, 37: 42-53.##Gilal, M. A.,& Chandio, R. A. (2013). Exchange Market Pressure and Intervention Index for Pakistan: Evidence from a Time-Varying Parameter Approach, GSTF Journal on Business Review (GBR), 2(4).  ##Girton, L. D., & Roper, E. (1977). A Monetary Model of Exchange Market Pressure Applied to the Postwar Canadian Experience, American Economic Review, 67(4): 537-548.##Gochoco-Bautista, M., & Bautista, C. (2005). Monetary Policy & Exchange Market Pressure: The Case of the Philippines, Journal of Macroeconomics, 27(3): 153-168.##Guidolin, M. (2012). Modelling, estimating and forecasting financial data under regime (Markov) switching, Working paper, Bocconi University.##Hadian, E., Owjimehr, S. (2014). Investigating the behavior of EMP index in Iran's economy using an autoregressive pattern with smooth transition (STAR), Quarterly Journal of Applied Economics Studies in Iran (AESI), (10):247-266.##Hamilton, J. D. (1990). Analysis of time series subject to changes in regime, Journal of econometrics, 45, 39-70.##Hansen, B. (1999). Testing for Linearity. Journal of Economic Surveys, 13: 551-576.##Hansen, B. E. (2000). Sample splitting and threshold estimation, Econometrica, 68, 575-603.##Keenan, D. M. (1985). A Tukey non-additivity-type test for time series nonlinearity,  Biometrika, 72: 39–44.##Kemme, D. M., Lyakir, G. (2011). From Peg to Float: Exchange Market Pressure and Monetary Policy Pressure in the Czech Republic, Review of International Economics, 19(1): 93-108.##Khiabani, N., Ghaljei, N. (2014). Exchange Regimes and Exchange Market Pressure in a Petroleum-Exporting Economy (Iran's case), Quarterly Journal of Applied Economics Studies in Iran (AESI), (3): 3-22.##Klassen, F., Jager, H. (2011). Definition-consistent measurement of exchange market pressure, Journal of International Money and Finance, 30: 74 - 95.##Koop, G. (1995). Parameter uncertainty and impulse response analysis, Journal of Econometrics, 72:135-149##Koop, G., Pesaran, M. H., Potter, S. M. (1996), Impulse response analysis in nonlinear multivariate models, Journal of Econometrics, 74:119-47.##Krolzig, H .M. (1997). Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Applications to Business Cycle Analysis (Lecture Notes in Economics and Mathematical Systems), Berlin, Germany: Springer Verlag, vol. 454.##Kumah, A.Y. (2007). A Markov-Switching Approach to Measuring Exchange Market Pressure, International Journal of Finance and Economics, 16: 114-130.##Lestano, (2010). A Structural VAR Model of Exchange Rate Market Pressure: The Case of Indonesia, Majalah Ekonomi, 20(1).##Lo, M. C.,& Zivot, E. (2001). Threshold Cointegration and Nonlinear Adjustment to the Law of One Price,  Macroeconomic Dynamics, 5: 533-76.##Pandy, A. (2012). Impact of Monetary Policy on Exchange Market Pressure: The Case of Nepal, Journal of Asian Economics, 37:59-71.##Roper, D. E., Turnovsky, S. J. (1980). Optimal Exchange Market Intervention in a Simple Stochastic Macro Model, Canadian Journal of Economics, 13(2): 296-309.##Tong, H. (1983). Threshold models in non-linear time series analysis. Lecture notes in statistics, No. 21.##Tsay, R.S. (1986). Nonlinearity test for time series, Biometrika, 73:461–466.##Tsay, R. S. (1998). Testing and modeling multivariate threshold models, Journal of American Statistical Association, 93:1188-1202.##Turnovsky, S. J. (1985). Optimal Exchange Market Intervention: Two Alternative Classes of Rules", In: Bhandari, J.S.(ed) Exchange Rate Management Under Uncertainty, Cambridge: MIT Press.##Weymark, D. N. (1995). Estimating Exchange Market Pressure & the Degree of Exchange Market Intervention for Canada, Journal of International Economics, 39(3-4): 273-295.##Weymark, D. N. (1997a). Measuring the Degree of Exchange Market Intervention in a Small Open Economym, Journal of International Money and Finance, 16(1):55-79.##Weymark, D. N. (1997b). Measuring Exchange Market Pressure and Intervention in Interdependent Economy:A Two-Country Model, Review of International Economics, 5(1):72-82.##Weymark, D. N. (1998). A General Approach to Measuring Exchange Market Pressure, Oxford Economic Papers, 50(1): 106-121.##Ziaei, S. M. (2012). Evaluating the Market Exchange Rate Pressure in Inflation Condition (An Empirical Evidence of Iran), Journal of Basic and Applied Scientific Research, 3(11):304-309##

1 چکیده های فارسی Persian Abstracts https://ijes.shirazu.ac.ir/article_6102.html 10.22099/ijes.2640.6102 1 0 - 0 0