An Estimation of Seasonal GDP Gap in Iran: Application of Adaptive Least Squares Method

Arash Hadizadeh, Ahmad Jafari Samimi and Zahra Mila Elmi
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Arash Hadizadeh: Ph.D. student of economics at The University of Mazandaran, Babolsar, Iran.
Zahra Mila Elmi: Associate professor of economics The University of Mazandaran, Babolsar ,Iran

Iranian Economic Review (IER), 2013, vol. 18, issue 1, 157-177

Abstract: This paper estimates the long-term trend of seasonal real GDP in Iran, using a new econometric technique called Adaptive Least Squares (ALS). ALS is a special case of Kalman Filter that allows a time-varying parameter model to be estimated relatively easy. The estimated trend is used to proxy the output gap. Since the coefficients of the GDP lags are significantly different from zero, the model with intercept and trend and with three lags of the dependent variable has been tested in this article. The comparison of the results of ALS, OLS, HP and Kalman Filter show that the ALS method provides a better estimate. Therefore, it is suggested that the output gap estimation method provided in this paper be used in dealing with the monetary policies.

Keywords: Adaptive Least Squares; Iran; Output Gap; seasonal data (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eut:journl:v:18:y:2013:i:1:p:157

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