Estimation and Analysis of Output Gap: An Application of Structural Vector Autoregression and Hodrick-Prescott-Fmethods
Saeed Dehghan Khavari and Seyed Hossein Mirjalili
DOI : 10.3844/ajebasp.2012.180.189
American Journal of Economics and Business Administration
Volume 4, 2012
In this study we examined output gap in the Iranian economy. The main question of the study is that how much is seasonal output gap in Iranian Economy and which factor affects gap variation. The other question is that whether using HP-F as a statistical based method for estimating output gap, provide different result than using SVAR as theory based method. Accordingly the aim of study is to estimate potential output and thus output gap using two method and analysis of the result. We used two methods (Hodrick-Prescott Filter and SVAR) to estimate quarterly output gap for the period 1988:1-2008:4. The results pointed out that the estimation is not sensitive to the method and there is a close relation between oil revenue and output gap. In the period of 1998:3-1999:3, when oil price reduced to $11.45 per barrel, Iranian economy faced with a recession and it affected on output gap with a lag. Output gap increased from 34818 in 2004:1-76782 million dollars in 2008: 4. The comparison of estimated output gap and changes of oil price in different periods point out the positive relation. According to the estimations of output gap, output gap in the Iranian Economy has intense fluctuation due to the effects of oil proceeds fluctuations. In some years, actual output is more than potential output, that is, output gap is positive and so this situation can be an important reason for inflation in that period and policy maker must do plans and policies for control of inflation and in some years, actual output is less than potential output and this means output gap was negative. This situation is a reason for unemployment in these years and therefore policy makers must do expansionary policies.
© 2012 Saeed Dehghan Khavari and Seyed Hossein Mirjalili. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.