COMPARING MULTIVARIATE MODELS’ FORECASTS OF INFLATION FOR BRICS AND OPEC COUNTRIES
Authors: Ajayi Olaoluwa Vincent 
Published: November 2019

Purpose – This study identifies the most appropriately selected multivariate model for forecasting inflation in different economic environments. In specifying the multivariate models, the study test for the orders of integration of variables and for those that are nonstationary. For non-stationary variables, this study examines whether they are cointegrated. Engle and Granger (1987) establish that a cointegrating equation can be represented as an error correction model that incorporates both changes and levels of variables such that all of the elements are stationary. However, VARs estimated with cointegrated data will be misspecified if all of the data are differenced because long-run information will be omitted, and will have omitted stationarity inducing constraints if all the data are used in levels. Further, including variables in both levels and differences should sat-isfy stationarity requirements. However, they will omit cointegrating restrictions that may improve the model. Of course, these constraints will be satisfied asymptotically; but efficiency gains and improved multi-step forecasts may be achieved by imposing the constraints (Engle and Granger 1987, p. 259). Therefore, this study test for order of integration and compare inflation forecasting performance of different multivariate models for BRICS and OPEC countries.

Research methodology – The following approaches were considered; the first approach is to construct a VAR model in differences (stationary form) to forecast inflation. The second approach is to construct a VECM without imposing cointegrating restrictions. The third approach is to construct a VEC that imposes cointegrating restrictions on the VECM. This will help to understand whether imposing cointegrating restrictions via a VEC improves long-run forecasts.

Research limitation – The proposed multivariate models focused on differencing and cointegrating restrictions to ensure the stationarity of the data, the available variables were combined and specified based on their level of integration to forecast inflation. For instance, a VAR model is estimated based on differenced variables I(0); the same holds true for VECM and VEC models, where differenced variables and linear combinations of I(I) covariates are stationary. In future, multivariate models guided by economic theory rather than the order of integration of variables are suggested.

Findings – The result shows that the forecast performance of inflation depends on the nature of the economy and whether the country experiencing higher inflation or low inflation. For instance, the model that includes long-run information in the form of a specified cointegrated equation generally improves the inflation forecasting performance for BRICS countries and one OPEC country (Saudi Arabia) that has a history of low inflation.

Practical implications – This research will improve the policy makers decision on how to select appropriate model to forecast inflation over different economic environment.

Originality/Value – These methods have not been used to forecast inflation for many emerging economies such as OPEC and BRICS countries despite the importance of many of these countries to the global economy. This study fills this gap by evaluating the forecasting performance of inflation using multivariate VAR and cointegrating models for OPEC and BRICS economies.