The Evaluation of Global Accuracy of Romanian Inflation Rate Predictions Using Mahalanobis Distance


  • Mihaela SIMIONESCU Romanian Academy


The purpose of this study is to emphasize the advantages of Mahalanobis distance in assessing the overall accuracy of inflation predictions in Romania when two scenarios are proposed at different times by several experts in forecasting or forecasters using data from a survey (F1, F2, F3 and F4). Mahalanobis distance evaluates accuracy by including at the same time both scenarios and it solves the problem of contradictory results given by different accuracy measures and by separate assessments of different scenarios. The own econometric model was proposed to make inflation rate forecasts for Romania, using as explanatory variables for index of consumer prices the gross domestic product, index of prices in the previous period and the inverse of unemployment rate. According to Mahalanobis distance, in 2012 and 2013 F1 registered the highest forecasts accuracy distance. The average distance shows that F1 predicted the best the inflation rate for the entire period. F2 provided the less accurate prognoses during 2011-2013. According the traditional approach, based on accuracy indicators that were evaluated separately for the two scenarios, F1 forecasts provided the lowest mean absolute error and the lowest root mean square error for both versions of inflation predictions. All the forecasts of the inflation rate are superior as accuracy of naïve predictions.  However, according to U1 Theil’s coefficient and mean error, F3 outperformed all the other experts and also the forecasts based on own econometric model.


Ang, A., Bekaert, G., and Wei, M. (2007). Do macro variables, asset markets, or surveys forecast inflation better?. Journal of Monetary Economics, 54(4), 1163-1212.

Artis, M., and M. Marcellino. (2001). Fiscal Forecasting: The track record of the IMF, OECD and EC. The Econometrics Journal, 4(1), 30−36.

Ash, J.C.K., Smyth, D.J., and Heravi, S.M. (1998). Are OECD forecasts rational and useful?: A directional analysis. International Journal of Forecasting, 14(3), 381−391.

Barnichon, R., and C. J. Nekarda. (2013). The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market. Finance and Economics Discussion Series (FEDS), Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington (D.C.), 2013−2019.

Blix, M., Wadefjord, J., Wienecke, U., and M. Ådahl. (2001). How Good is the Forecasting Performance of Major Institutions. Sveriges Riksbank Economic Review, 3(2001), 37−67.

Bauer, A., Eisenbeis, R.A., Waggoner, D.F., and Zha, T. (2003). Forecast evaluation with cross-sectional data: The Blue Chip Surveys. Economic Review-Federal Reserve Bank of Atlanta, 88(2), 17-32.

Bauer, A., Eisenbeis, R.A., Waggoner, D.F., and Zha, T.A. (2006).Transparency, expectations, and forecasts. Working Paper, 5(1), 1-18.

Bowles, C., Friz, R., Genre, V., Kenny, G., Meyler, A., and Rautanen, T. (2010). An Evaluation of the Growth and Unemployment Forecasts in the ECB Survey of Professional Forecasters. Journal of Business Cycle Measurement and Analysis, 2010(2), 63−90.

Bratu, M. (2012). The reduction of uncertainty in making decisions by evaluating the macroeconomic forecasts performance in Romania. Economic Research–Ekonomska Istraživanja, 25(2), 239-262.

Caunedo, J., DiCecio, R., Komunjer, I., & Owyang, M.T. (2013). Federal reserve forecasts: asymmetry and state-dependence. Federal Reserve Bank of St. Louis Working Paper Series, 2013-012.

Clements, M.P., Joutz, F., and Stekler, H.O. (2007). An evaluation of the forecasts of the Federal Reserve: a pooled approach. Journal of Applied Econometrics, 22(1), 121-136.

Clements, M.P. (2014). Forecast Uncertainty—Ex Ante and Ex Post: US Inflation and Output Growth. Journal of Business & Economic Statistics, 32(2), 206-216.

Davies, A., and Lahiri, K. (1999). Re-examining the rational expectations hypothesis using panel data on multi-period forecasts. Analysis of Panels and Limited Dependent Variable Models, Cambridge: Cambridge University Press.

Dovern, J. (2014). A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data. Discussion Papers Series, 571, 1-32.

Glück, H., and Schleicher, S.P. (2005). Common Biases in OECD and IMF Forecasts: Who Dares to be Different? Paper presented at the workshop A Real Time Database for the Euro-Area, 13−14 June 2005, Brussels.

Hyndman, R. J., and Athanasopoulos, G. (2014). Forecasting: principles and practice. Melbourne: OTexts.

Hyndman, R. J., and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4), 679-688.

Koutsogeorgopoulou, V. (2000). A Post-Mortem on Economic Outlook Projections. OECD Economics Department Working Papers, no. 274, 1-34.

Müller-Dröge, C., Sinclair, T., and Steckler, H.O. (2014). Evaluating Forecasts of a Vector of Variables: a German Forecasting Competition, Cama Working Paper, 55(1), 1-38.

Öller, L.E., and Barot, B. (2000). The accuracy of European growth and inflation forecasts. International Journal of Forecasting, 16(3), 293-315.

Sinclair, T. M., Stekler, H.O., and Carnow, W. (2015). Evaluating a vector of the Fed’s forecasts. International Journal of Forecasting, 31(1), 157-164.

Sinclair, T.M., Stekler, H.O., and Kitzinger, L. (2010). Directional forecasts of GDP and inflation: a joint evaluation with an application to Federal Reserve predictions. Applied Economics, 42(18), 2289-2297.

Simionescu, M. (2013). Incertitudinea previziunilor în modelarea macroeconomică [The uncertainty of forecasting in macroeconomic modeling]. Bucharest: Universiară Publishing House.

Swanson, D.A., Tayman, J., and Bryan, T.M. (2011). MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts. Journal of Population Research, 28(2-3), 225-243.

Timmermann, A. (2007). An Evaluation of the World Economic Outlook Forecasts. IMF Staff Papers, 54(1), 1-33.

Vogel, L. (2007). How Do the OECD Growth Projections for the G7 Economies Perform? A Post-Mortem. OECD Economics Department Working Paper, no. 573, 1-33.




How to Cite

SIMIONESCU, M. (2015). The Evaluation of Global Accuracy of Romanian Inflation Rate Predictions Using Mahalanobis Distance. Management Dynamics in the Knowledge Economy, 3(1), 133. Retrieved from