Monetary economics
Elham Kamal; Vahid Taghinejadomran
Abstract
This paper studies the main fiscal determinants of central bank credibility (CBC) from 1990 to 2014. Covering 25 inflation-targeting (IT) economies, we mainly focus on sovereign debt holders and fiscal rules since adopting the IT framework. As the CBC indicator is highly concentrated in the right tail ...
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This paper studies the main fiscal determinants of central bank credibility (CBC) from 1990 to 2014. Covering 25 inflation-targeting (IT) economies, we mainly focus on sovereign debt holders and fiscal rules since adopting the IT framework. As the CBC indicator is highly concentrated in the right tail of the distribution, the mean-based approaches are incapable of unearthing the fact that the effect of fiscal factors may be asymmetric across the distribution of the credibility index. In departing from the problem, we use a quantile regression method to estimate parameters over the entire conditional distribution of CBC. The asymmetric response using the quantile regression is state-dependent and conditional on the credibility distribution. Having provided a comprehensive survey on the fiscal factors potentially related to the credibility in the literature, we find that fiscal rules are almost prominent at the lower quantiles while debt holders' composition is strongly significant at the upper tails of CBC distribution. These findings are further supported by the slope equality tests, discussed in Koenker & Bassett (1982). These results could be attributed to the more sensitivity of the private sector expectations to the debt holders’ composition. Therefore, central bankers could reduce public expectations by taking into account the non-linear impact of fiscal factors on their credibility.
Afsaneh Kazemi Mehrabadi; Vahid Taghinezhad Omran; Mohammad Valipour Khatir; Saeed Rasekhi
Abstract
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 ...
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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.
