Esmaeil Naderi - Academia.edu (original) (raw)
Uploads
Papers by Esmaeil Naderi
A business cycle is, in fact, fluctuations of macroeconomic variables and gross domestic product.... more A business cycle is, in fact, fluctuations of macroeconomic variables and gross domestic product. These fluctuations play a substantial role in any country. Prosperity and depression have been the most impressive problem in Iranian economy during the last decades so government and politicians have always sought a remedy for alleviating its negative effects like inflation and unemployment. This study analyses the underlying causes of Iranian business cycles using structural auto regression (SVAR) in the period between 1965-2009. The findings of this research show that business cycle in oil exporting countries is affected by changes in oil revenues. To identifying how oil shocks spread through different variables we use Bernanke and Sims (1997) technique, imposing a set of long-run economic restrictions that are added to purely statistical restrictions of VAR. In the end, the hypothesis of the thesis verifies that the effect of fiscal policy in generating business cycles is much more than monetary policy and technological shock. But, bear in mind that the effect of technology shock in Iranian economy, in general, could not be ignored. JEL Classification: E32،E52،E62،O39.
International Journal of Economics and Financial Issues, Jan 30, 2013
This study is an attempt to review the theory and applications of autoregressive fractionally int... more This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed persistence in the mean and volatility of a time series. The long memory feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time series. The daily data related to Tehran Stock Exchange (TSE) index was used for the purpose of this study. Considering the fact that the existence of conditional heteroskedasticity effects were confirmed in the stock return series, robust regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also compared. The results of ARFIMA model are indicative of the absence of long memory in return series of the TSE index and the results from FIGARCH model show evidence of long memory in conditional variance of this series.
A business cycle is, in fact, fluctuations of macroeconomic variables and gross domestic product.... more A business cycle is, in fact, fluctuations of macroeconomic variables and gross domestic product. These fluctuations play a substantial role in any country. Prosperity and depression have been the most impressive problem in Iranian economy during the last decades so government and politicians have always sought a remedy for alleviating its negative effects like inflation and unemployment. This study analyses the underlying causes of Iranian business cycles using structural auto regression (SVAR) in the period between 1965-2009. The findings of this research show that business cycle in oil exporting countries is affected by changes in oil revenues. To identifying how oil shocks spread through different variables we use Bernanke and Sims (1997) technique, imposing a set of long-run economic restrictions that are added to purely statistical restrictions of VAR. In the end, the hypothesis of the thesis verifies that the effect of fiscal policy in generating business cycles is much more than monetary policy and technological shock. But, bear in mind that the effect of technology shock in Iranian economy, in general, could not be ignored. JEL Classification: E32،E52،E62،O39.
International Journal of Economics and Financial Issues, Jan 30, 2013
This study is an attempt to review the theory and applications of autoregressive fractionally int... more This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed persistence in the mean and volatility of a time series. The long memory feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time series. The daily data related to Tehran Stock Exchange (TSE) index was used for the purpose of this study. Considering the fact that the existence of conditional heteroskedasticity effects were confirmed in the stock return series, robust regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also compared. The results of ARFIMA model are indicative of the absence of long memory in return series of the TSE index and the results from FIGARCH model show evidence of long memory in conditional variance of this series.