Evaluating the Performance of Autoregressive Model in Forecasting Iranian Inflation

In this paper the performance of iterated and direct autoregressive models in forecasting Iranian inflation has been evaluated in horizons 1, 2, 3 and 4 steps ahead. The results show that the forecast accuracy of direct method compared to iterated method depends on the information criteria. In forecasting literature, lag selection is done as cumulative. This paper also investigate whether the use of all possible combination of lags, rather than using cumulative lags can lead to improve forecast accuracy. Our findings show that the optimal combination of lags changes depending on forecast horizon, so that the best combination of lags in the horizon 1 and 2 is the first lag, and in the horizon 3 and 4, are the first and fourth lags. Also using IC method to reduce systematic error does not improve forecast accuracy.