Forecasting South African food inflation
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Date
2015-01
Authors
Griessel, Günther Diederich
Journal Title
Journal ISSN
Volume Title
Publisher
University of the Free State
Abstract
Since the sharp increase in food prices, both domestically and internationally, in 2008/2009 the need to forecast food inflation has become more and more prominent, especially in developing countries. This is because a higher percentage of household income is spent on food in these countries. Food inflation therefore, plays an important role in overall inflation in South Africa and ultimately affects monetary policy decisions.
The primary objective of this study was to fit a multivariate model for the food component of the South African Consumer Price Index (CPI), so as to forecast food inflation in South Africa. Various models were identified but the Vector Autoregressive model was deemed suitable as per literature.
A food inflation forecasting model was developed with CPI without the food component, nominal effective exchange rate, money supply, domestic food supply balance sheet, oil prices, producer price index, SARB repurchase rate and international food prices included as independent variables, as prescribed by literature reviewed. These data were entered at monthly intervals.
Forecasting of data involves understanding the short run linkages between variables. This was captured by means of impulse response functions and forecast error variance decomposition. In the short run it was found that shocks in nominal effective exchange rate, gross domestic product and CPI without the food component explained the majority of variance in food inflation. To determine long run cointegrating relationships between the variables, Johansen cointegration testing was carried out. With the presence of cointegrating variables, a Vector Error Correction Model was constructed for forecasting purposes.
Sample forecasts were then made and compared with actual data in order to determine current accuracy of the model in terms of deviation from currently available data at the time of writing. The model was then simply solved for two years ahead to produce a two year-ahead forecast of South African food inflation. The resulting forecasts yielded an expected food inflation index to reach 117.75 index points in a years’ time (May 2014 to May 2015) and 125.58 index points in two years’ time (May 2014 to May 2016).
The need for construction of a more representative CPI for South Africa was identified, but is beyond the scope of this study.
Description
Keywords
Vector autoregressive model, Vector error correction model, Inflation, Food Inflation, Forecasting, Monetary Policy, Food prices -- South Africa, Food security -- South Africa, Food supply -- South Africa, Food -- South Africa -- Economic conditions, Food supply -- South Africa -- Forecasting, Economic forecasting, Dissertation (M.Sc.Agric. (Agricultural Economics))--University of the Free State, 2015