Determinants of Food Inflation in Pakistan &Effects of Seasonal Adjustment On Forecasting Food Inflation

Abstract

Prices of food are sky-rocketting all over the world but the plight of the developing countries like Pakistan is quite miserable and the people who are already living below poverty line can hardly manage to make both ends meet. So in order to klnow the main causes of this fiasco we decided to conduct this research. The main focus of this project was to find out the factors influencing food inflation in Pakistan. After highlighting these variables a hybrid monetarist-structuralist model for food inflation was formulated. After performing standard unit root testing procedures a test was conducted for the presence of seasonal unit roots and all the variables needed seasonal adjustment. A detailed analysis of the long-run relationships between food prices and its determinants and causal nexus between them has been conducted both prior to and after seasonal adjustment using Demetra. A comparison of both the approaches shows that the results become more pragmatic when seasonal factor has been accounted for.Granger causality tests on the actual data show that food prices granger cause money supply, energy prices and wheat support price.Whereas exchange rate and value added in agriculture granger cause food prices. But when the data was adjusted for seasonality we came to know that two-way causality exists between value added in agriculture and food prices. Similarly Wheat Support Prices and Food Prices also have reverse causal links and both play equal part in determining each other.Besides two-way causal nexus we also notice that food prices granger cause energy prices and food exports. In addition to this it was found that Exchange rate, Fertilizer prices, Food imports and Money supply all granger cause food prices. Johansen cointegration test showed the existence of six cointegration vectors and this number was reduced to four when the data was adjusted for seasonality.As far as forecasting food inflation is concerned it is manifested by the results that forecast efficiency of the model improves when the data has been dealt with seasonal adjustment.