VARMA versus VAR for Macroeconomic Forecasting
In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real macroeconomic data and show that VARMA models forecast macroeconomic variables more accurately than VAR models.
|Date of creation:||Jan 2006|
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- George Athanasopoulos & Farshid Vahid, 2006.
"A Complete VARMA Modelling Methodology Based on Scalar Components,"
Monash Econometrics and Business Statistics Working Papers
2/06, Monash University, Department of Econometrics and Business Statistics.
- George Athanasopoulos & Farshid Vahid, 2008. "A complete VARMA modelling methodology based on scalar components," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 533-554, 05.
- Lutkepohl, Helmut & Poskitt, D S, 1996.
"Specification of Echelon-Form VARMA Models,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 14(1), pages 69-79, January.
- Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
- Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.
- Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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