Performance of Alternative Price Forecast for Pakistan

Yaser Javed and Eatzaz Ahmad

Abstract

To evaluate the price forecasts, we use two data frequencies i.e., annual and quarter with two most demanding techniques, i.e., ARIMA and VAR models to forecast the four index of inflation, named, Consumer Price Index (CPI), Wholesale Price Index (WPI), GNP Price Deflator (GNPPD), and Implicit Price Deflator of Total Domestic Absorption (DAPD).2 In order to test the performance of price forecast for Pakistan, we found Consumer Price Index (CPI) and Implicit Price Deflator of Total Domestic Absorption (DAPD) better than Wholesale Price Index (WPI) and GNP Price Deflator (GNPPD). In general more elaborate Vector Autoregressive (VAR) models outperform the simplistic Auto Regressive Integrated Moving Average (ARIMA) models in forecasting a price series. Another useful conclusion is that the quarterly data provide better forecasts than the annual data. All these results support the econometricians’ maintained hypotheses that, data observed at high frequency and statistically more elaborate use of a given data set provides better predictions than the data observed at low frequency and analyzed with simplistic statistical tools.