Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting

Branimir, Jovanovic and Magdalena, Petrovska (2010): Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting. Published in: National Bank of the Republic of Macedonia Working Paper (August 2010)

Abstract

We evaluate the forecasting performance of six different models for short-term forecasting of Macedonian GDP: 1) ARIMA model; 2) AR model estimated by the Kalman filter; 3) model that explains Macedonian GDP as a function of the foreign demand; 4) small structural model that links GDP components to a small set of explanatory variables; 5) static factor model that links GDP to the current values of several principal components obtained from a set of high-frequency indicators; 6) FAVAR model that explains GDP through its own lags and lags of the principal components. The comparison is done on the grounds of the Root Mean Squared Error and the Mean Absolute Error of the one-quarter-ahead forecasts. Results indicate that the static factor model outperforms the other models, providing evidence that information from large dataset can indeed improve the forecasts and suggesting that future efforts should be directed towards developing a state-of-the-art dynamic factor model. The simple model that links domestic GDP to foreign demand comes second, showing that simplicity must not be dismissed. The small structural model that explains every GDP component as a function of economic determinants comes third, “reviving” the interest in these old-school models, at least for the case of Macedonia.

• Item Type: MPRA Paper
• Language: English
• Keywords: GDP; forecasting; structural model; principal component; FAVAR; static factor model; Macedonia
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Barhoumi, K., Darné, O. and Ferrara, L. (2009). "Are disaggregate data useful for factor analysis in forecasting French GDP?," Documents de Travail 232, Banque de France.

Bai J. and Ng S. (2002). "Determining the number of factors in approximate factor models". Econometrica, 70, 191-221.

Bernanke, Ben, Jean Boivin and Piotr S. Eliasz (2005). "Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, MIT Press, vol. 120(1), pages 387-422, January.

Boivin J. and Ng S. (2006). "Are more data always better for factor analysis?", Journal of Econometrics, 132, 169-194.

Box, G. E. P. and Jenkins G.M. (1976). Time Series Analysis: Forecasting and Control, San Francisco CA, Holden Day.

Carriero, A, G. Kapetanios and M. Marcellino (2007). "Forecasting large datasets with reduced rank multivariate models", Working paper No. 617, Queen Mary University of London

Del Negro,Marco and F Schorfheide (2004): "Priors from equilibrium models for VARs", International Economic Review, Vol. 45, pp 643-673.

Diebold F.X. and Mariano R.S. (1995). "Comparing predictive accuracy". Journal of Business and Economic Statistics, 13, 253-265.

Doz C., Giannone D. and Reichlin L. (2006). "A quasi maximum likelihood approach for large approximate dynamic factor models". CEPR Discussion Paper No. 5724.

Doz C., Giannone D. and Reichlin L. (2007). "A two-step estimator for large approximate dynamic factor models based on Kalman filtering". CEPRDiscussion Paper No 6043.

Felix, R. M. and L. C. Nunes (2003). "Forecasting Euro Area Aggregates with Bayesian VAR and VECM Models". Working Paper 4-03, Banco De Portugal

Forni M., Hallin M., Lippi M. and Reichlin L. (2000). "The generalized dynamic factor model: Identification and estimation". Review of Economics and Statistics, 82, 540-554.

Forni M., Hallin M., Lippi M. and Reichlin L. (2004). "The generalized dynamic factor model: Consistency and rates". Journal of Econometrics, 119, 231-255.

Forni M., Hallin M., Lippi M. and Reichlin L. (2005). "The generalized dynamic factor model: One-sided estimation and forecasting". Journal of the American Statistical Association, 100, 830-840.

Goodfriend, Marvin (2007). "How the World Achieved Consensus on Monetary Policy," NBER Working Papers 13580, National Bureau of Economic Research, Inc.

Hamilton, James D. (1994), “Time Series Analysis”, Princeton University Press

Harvey, Andrew C. (1989), “Forecasting, Structural Time Series Models and the Kalman Filter”, Cambridge University Press

Harvey, Andrew C. (1993), “Time Series Models”, Harvester Wheatsheaf, Second Edition

Harvey, Andrew (2006). "Forecasting with Unobserved Components Time Series Models", in Handbook of Economic Forecasting, vol. 1, Elliott, G, C. W. J. Granger and A. Timmermann, eds. North Holland

Harvey, D. I., Leybourne, S. J., and Newbold, P. (1997), "Testing the Equality of Prediction Mean Squared Errors," International Journal of Forecasting, 13, 281-291.

Litterman, Robert B. (1986). “Forecasting With Bayesian Vector Autoregressions: Five Years of Experience.” Journal of Business and Economic Statistics.January, 4:1, pp. 25–38.

Mankiw, N. Gregory (1991). "A Quick Refresher Course in Macroeconomics," NBER Working Papers 3256, National Bureau of Economic Research, Inc.

Mankiw, N. Gregory (2006)."The Macroeconomist as Scientist and Engineer," NBER Working Papers 12349, National Bureau of Economic Research, Inc.

Sims, Christopher A. (1980). "Macroeconomics and Reality," Econometrica, vol. 48(1), pages 1-48, January.

Sims, Christopher A. (1993). “A Nine Variable Probabilistic Macroeconomic Forecasting Model,” in NBER Studies in Business: Business Cycles, Indicators, and Forecasting, Volume 28. James H. Stock and Mark W. Watson, eds. Chicago: University of Chicago Press, pp. 179–214.

Sims, Christopher A. and Tao Zha (1996). "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.

Smets, F and R Wouters (2007): "Shocks and frictions in US business cycles: a Bayesian DSGE approach",The American Economic Review, Vol 97, No. 3, pp. 586-606.

Stock, James H., and Mark W. Watson (2001). “Vector Autoregressions,” Journal of Economic Perspectives, Vol. 15 No. 4, pp. 101-115.

Stock, J.H. and M. Watson, (2002), "Forecasting using principal components from a large number of predictors". Journal of the American Statistical Association 97, 1167-1179.

Stock, James H., and Mark W. Watson (2006). "Forecasting with Many Predictors", in Handbook of Economic Forecasting, vol. 1, Elliott, G, C. W. J. Granger and A. Timmermann, eds. North Holland

Thomas Doan, Robert Litterman and Christopher Sims (1984). "Forecasting and conditional projection using realistic prior distributions", Econometric Reviews 1, pp. 1-100

Tovar, Camilo E. (2008), "DSGE Models and Central Banks", BIS Working Paper No. 258

Watson M. (2003). "Macroeconomic forecasting using many predictors". In Dewatripont M., Hansen L. and Turnovsky S. (eds), Advances in Economics and Econometrics, Theory and Applications: Eighth World Congress of the Econometric Society, Vol. III.

Woodford, M. (1999). "Revolution and Evolution in Twentieth-Century Macroeconomics," , Presented at a conference, Frontiers of the Mind in the Twenty-First Century, U.S. Library of Congress, Washington, D.C., June 1999.