Comparing multivariate models’ forecasts of inflation for BRICS and OPEC countries

    Olaoluwa Vincent Ajayi Affiliation


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.

Keyword : inflation forecasting, cointegrating and stability tests

How to Cite
Ajayi, O. V. (2019). Comparing multivariate models’ forecasts of inflation for BRICS and OPEC countries. Business, Management and Economics Engineering, 17(2), 152-172.
Published in Issue
Nov 8, 2019
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Agtmael, A. (2012). Think again: the BRICS. Retrieved from

Alles, L., & Horton, D. (2002). An evaluation of alternative methods of forecasting Australian inflation. The Australian Economic Review, 32(3), 237-248.

Atkeson, A., & Ohanian, L. (2001). Are Phillips curves useful for forecasting inflation? Federal Reserve Bank of Minneapolis Quarterly Review, 25(2001), 2-11.

Bjornland, H., Jore, A., Smith, C., & Thorsrud, L. (2008). Improving and evaluating short term forecasts at the Norges Bank. Norges Bank Staff Memo, No 4.

Buelens, C. (2012). Inflation forecasting and the crisis: assessing the impact on the performance of different forecasting models and methods. Economic papers 451/March2012.

Canova, F. (2007). G-7 inflation forecast: random walk, Phillips curve or what else? Macroeconomic Dynamic, 11, 1-30.

Chen, S. (2009). Oil price pass- through into inflation. Energy Economics, 31(1), 126-133.

Christoffersen, P., & Diebold, F. (1998). Cointegration and long- horizon forecasting. Journal of Business and Economics Statistics, 16(4), 450-458.

Clark, T., & McCracken, M. (2006). The predictive content of the output gap for inflation: resolving in sample and out of sample evidence. Journal of Money, Credit and Banking, 38(5), 1127-1148.

Cologni, A., & Manera, M. (2008). Oil price inflation and interest rates in a structural cointegrated VAR model for G- 7 countries. Energy Economics, 30, 856-888.

Dedeoglu, D., & Kaya, H. (2014). Pass- through of oil price to domestic prices: Evidence from an oil- hungry but oil-poor emerging market. Economic Modelling, 43(2014), 67-74.

Dotsey, M., Fujita, S., & Stark, T. (2011). Do Phillip curve conditionally help to forecast inflation. Working Paper Research Department, Federal Reserve Bank of Philadelphia.

Energy Information Administration. (2013). What drives crude oil prices? Independent Statistic Analysis. Retrieved from

Engle, F., & Yoo, B. (1987). Forecasting and testing in co-integrated systems. Journal of Econometrics, 35(1987), 143-159.

Engle, R., & Granger, C. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica, 55(2), 251-276.

Fanchon, P., & Wendel, J. (1992). Estimating VAR models under non – stationarity and co-integration: Alternative approaches for forecast cattle price. Applied Economics Journal, 1992(24), 207-217.

Fritzer, F., Moser, G., & Scharler, J. (2002). Forecasting Austrian HICP and its Component using VAR and ARIMA models. OeNB Working Paper 73, July 2002. Retrieved from

Gabrielyan, D. (2016). Forecasting inflation using the Phillips curve: evidence from Swedish data. The University of Tartu Feba, ISSN-L 1406- 5967.

Garcia, M., Medeiros, M., & Vasconcelos, G. (2017). Real-time inflation forecasting with high- dimensional models: The case of Brazil. International Journal of Forecasting, 33(2017), 679-693.

Global Sherpa. (2014). Globalization, sustainable development and social impact in world rankings, countries and cities. Retrieved from

Gupta, R., Eyden, R., & Waal, A. (2015). Do we need a global VAR model to forecast inflation and output in South Africa? Applied Economics, 47(25), 2649.

Hoffman, D., Anderson, R., & Rasche, R. (2002). A vector error-correction forecasting model of the US economy. Journal of Macroeconomic, 24, 560-598.

Hooker, M. (2002). Are oil shocks inflationary? Asymmetric and nonlinear specification versus change in regime. Journal of Money, Credit, and Banking, 34(2), 540-561.

Kelikume, I., & Salami, A. (2014). Time series modelling and forecasting inflation: evidence from Nigeria. The International Journal of Business and Finance Research, 8(2), 41-51.

Lack, C. (2006). Forecasting swiss inflation using VAR models. Swiss National Bank Economics Studies, No. 2. ISSN 1661-142X.

LeBlance, M., & Chinn, M. (2004). Do high oil prices presage inflation? The evidence from G-5 countries. UC Santa Cruz Economics Working Paper No. 561; SCCIE Working Paper No. 04-04.

Lee, U. (2012). Forecasting inflation for targeting countries: A comparison of the predictive performance of alternative inflation forecast model. Journal of Business Economics Studies, 18(1), 75-95, 136.

Mandal, K., Bhattcharya, I., & Bhoi, B. (2012). Is the oil price pass- through in India any different? Journal of Policy Modelling, 34(2012), 832-848.

Mitra, D., & Rashid, M. (1996). Comparative accuracy of forecasts of inflation: A Canadian Study. Applied Economics, 28(12), 1633-1637(5).

Nadal-De Simone, F. (2000) Forecasting inflation in Chile using state space and regime switching models. IMF Working Paper WP/00/162, Washington, DC. Retrieved from

Organization of the Petroleum Exporting Countries. (2019). Member countries. Retrieved from

Ogunc, F., Akdogan, K., Baser, S., Chadwick, M., Ertug, D., Hulagu, T., Kosem, S., Ozmen, M., & Tekali, N. (2013). Short-term inflation forecasting models for Turkey and a forecast combination analysis. Economic Modelling, 33(July 2013), 312-325.

Onder, O. (2004). Forecast inflation in emerging markets by using Philip curve and Alternative time Series model. Emerging Markets Finance and Trade, 40(2), 71-82.

Ozkan, H., & Yazgan, M. (2015). Is forecasting inflation easier under inflation targeting? Empirical Economics, 48(2), 609.

Perron, P. (1989). The great crash, the oil price shock and the unit root hypothesis. Econometrica, 57, 1361-1401.

Pretorious, C., & Rensburg, T. (1996). The forecast performance of alternative models of inflation. Occasional Paper No. 10. Pretoria: South African Reserve Bank.

Rossi, B. (2012). Advances in forecasting under instability. Working paper 11-20. Duke University, Department of economics.

Sa-ngasoongsong, A., Bukkapatnam, S., Kim, J., Iyer, P., & Suresh, P. (2012). Multi-step sales forecasting in automotive industry based on structural relationship identification. International Journal of Production Economics, 140(2012), 875-887.

Sarantis, N., & Stewart C. (1995). Monetary and asset market models for sterling exchange rates: a cointegration approach. Journal of Economic Integration, 10(3), 335-371.

Shan, C., & Ghonasgi, N. (2016). Determinants and forecast of price level in India: a VAR Framework. Journal of Quantitative Economics, 14, 57-86.

Shoesmith, L. (1992). Cointegration, error correction and improved regional VAR forecasting. Journal of Forecasting, 11, 91-109.

Shoesmith, L. (1995b). Multiple cointegrating vectors, error correction and forecasting with Litterman’s model. International Journal of Forecasting, 11, 557-567.

Shoesmith, L. (1995a). Long-term forecasting of non-cointegrated and cointegrated regional and national models. Journal of Regional Science, 35, 43-64.

Stock, H., & Watson, M. (1999). Forecasting Inflation. Journal of Monetary Economics, 44(2), 293-335.

Stock, H., & Watson, W. (2003). Forecasting output and inflation: the role of asset prices. Journal of Economic Literature, 4(3), 788-829.

Stock, H., & Watson, M. (2008). Philip curve inflation forecasts. Working Paper 14322. NBER.

Taylor, B. (2000). Low inflation, pass-through, and the pricing power of firms. European Economic Review, 44(7), 1389-1408.

The Goldman Saches Group. (2007). BRICS and beyond. Retrieved from

The Reuter. (2016). Algeria’s economic policy may accelerate inflation – IMF On line. Retrieved from

Timothy, D., & Thoma, A. (1998). Modelling and forecasting cointegrated variables: some practical experience. Journal of Economics and Business, 50, 291-307.

Vogelsang, T., & Perron, P. (1998). Additional tests for a unit root allowing for a break in the trend function at an unknown time. International Economic Review, 39(4), 1073-1100.

World Economic Outlook. (2019). Real GDP growth annual percentage change. International Monetary Fund. Retrieved from