فهرست مطالب reza heybati

Given that the Iranian economy is affected by different fluctuations and innovations, it is important to estimate a measure of macroeconomic uncertainty, which represents aggregate level of uncertainty in economics. This study provides a comprehensive time series measure of macroeconomic uncertainty for Iran, estimated separately for different forecast horizons. Moreover, it provides superior econometric estimate of time-varying macro uncertainty, and considers macro uncertainty movements over the period 1991–2015. The estimated measures of macro uncertainty, base-case and its alternatives, show that the important uncertainty episodes of the Iranian economy are associated with deep recessions. Specifically, the major spikes in the baseline estimate occurred during the 1992:1–1994:1, 1994:3–1995:2, and 2011:3-2013:3 recession periods. Finally, results of impulse responses show that the macro uncertainty innovations are followed by a significant persistent decrease in both investment and production, supporting the findings of long-lived negative effects of uncertainty.

In this study measuring of uncertainty related to macroeconomic variables has been considered. Given that uncertainty is not directly observable and measurable, researchers suggest different proxies for its measuring. One of the popular approaches in the related literature is based on time series models. In this approach, the proper measurement of uncertainty requires correct specification of regression equations. Accordingly, we estimate the uncertainty estimates of the baseline model for several key series in Iran’s macro dataset and compare it to the corresponding estimates of the alternative models. Our results show that uncertainty estimates of macro variables are affected by the specification of the forecasting regression equations. The difference over time between the estimates for these variables is quite pronounced in some periods, suggesting that much of the variation in these series is predictable and should not be attributed to uncertainty. Finally, evaluation of the uncertainty forecasting accuracy of the models shows that the SV and asymmetric GARCH models have better performance for the in-sample and out-of-sample periods, respectively.