Accuracy Assessement of Short Run Macroeconomic Forecasts in Romania

• Autorzy: Mihaela Bratu
• Języki publikacji: EN

Abstrakty

EN

Inflation rate, unemployment rate and interest rate are some of the most important indicators used at macroeconomic level. These variables present an important interest for the central banks that establish the monetary policy (inflation target), but also for the government interested in public policies. Macro-econometric modeling offers the advantage of using more models to describe the evolution of a single variable and also the advantage of predicting it. But it is important to choose the forecast with the higher degree of accuracy. Calculating some indicators of accuracy we may know the best forecast that will be used to establish the macroeconomic policies. For the interest rate and unemployment rate in Romania VAR (2) models generated more accurate forecasts than ARMA models or models with lags. For the inflation rate the model with lag, which is consistent with Granger causality, determined the most accurate forecasts. The predictions based on all these models are better than those got using smoothing exponential techniques. (original abstract)

Słowa kluczowe

EN

Macroeconomic situation; Macroeconomic forecasting; Econometric models

PL

Sytuacja makroekonomiczna; Prognozowanie makroekonomiczne; Modele ekonometryczne

• Czasopismo: Economics & Sociology
• Rocznik: 2012
• Tom: 5
• Numer: nr 1
• Strony: 26--38

Twórcy

autor

Mihaela Bratu

• Bucharest University of Economics Bucharest, Romania

Bibliografia

• Armstrong, J. S., Collopy F. (2000), Another Error Measure for Selection of the Best Forecasting Method: The Unbiased Absolute Percentage Error, International Journal of Forecasting, 8, pp. 69-80.
• Armstrong, J. S., Fildes R. (1995), On the selection of Error Measures for Comparisons Among Forecasting Methods, Journal of Forecasting, 14, pp. 67-71.
• Athanasopoulos, G., Vahid, F. (2005), A Complete VARMA Modeling Methodology Based on Scalar Components, working paper, Monash University, Department of Econometrics and Business Statistics.
• Bokhari, SM. H., Feridun, M. (2005), Forecasting Inflation through Econometrics models: An Empirical Study on Pakistani Data, The Information Technologist, Vol.2(1), pp. 15-21.
• Diebold, F.X. and Mariano, R. (1995),Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, pp. 253-265.
• Fildes, R., Steckler, H. (2000), The State of Macroeconomic Forecasting, Lancaster University EC3/99, George Washington University, Center for Economic Research, Discussion Paper No. 99-04.
• Hyndman, R. J., Koehler, A.B. (2005), Another Look at Measures of Forecast Accuracy, Working Paper 13/05, available at http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/
• Rosas, A.L., Guerrero, V.M. (1994), Restricted forecasts using exponential smoothing techniques, International Journal of Forecasting, Vol. 10, Issue 4, December 1994, pp. 515-527.
• Taylor, J.W. (2004), Smooth Transition Exponential Smoothing, Journal of Forecasting, Vol. 23, pp. 385-39.
• EUROSTAT (2012), Data base. [online] Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes [Accessed on March 2012].

Identyfikatory

DOI

10.14254/2071-789X.2012/5-1/2