Abstract
The purpose of the study is to forecast inflation in Pakistan from January to June 2008. This
study set out to redress the deficiency and explicitly use of time series techniques solely for
forecasting purposes. The analysis based on the time series available from July 1995 to
December 2007. Inflation is a trending series with the possibility that the trend is time varying.
It is also plausible that monthly inflation could move around a time varying mean. Forecasting
inflation is a difficult but essential task for the successful implementation of monetary policy.
Inflation forecasts are central to macroeconomic analysis. There are a number of approaches
available for forecasting economic time series. One approach, which includes only the time
series being forecast, is known as univariate forecasting. An alternative approach is
multivariate time series forecasting. To forecast inflation in Pakistan on monthly basis, we
segregated our study in two parts, univariate with ARIMA Model and multivariate with VAR
Model. Forecasted inflation for the month of January-08 and February-08 are close to the
actual inflation, while in March 2008 there is found significant differences in forecasted and
actual values of inflation. The reason of the high rate of actual inflation in March 2008 is the
rise in oil prices.
Azam Ali, Dr. S.M. Husnain Bokhari. (2009) Forecasting Inflation Using Univariate and Multivariate Time Series, Journal of Independent Studies and Research-Management, Social Sciences and Economics, Volume-07, Issue-1.
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