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Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models

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  • Albis, Manuel Leonard F.
  • Mapa, Dennis S.

Abstract

The estimated Vector AutoRegressive (VAR) model is sensitive to model misspecifications, such as omitted variables, incorrect lag-length, and excluded moving average terms, which results in biased and inconsistent parameter estimates. Furthermore, the symmetric VAR model is more likely misspecified due to the assumption that variables in the VAR have the same level of endogeneity. This paper extends the Bayesian Averaging of Classical Estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of Asymmetric VAR models, in order to achieve robust results. The combination of the two procedures is deemed to minimize the effects of misspecification errors by extracting and utilizing more information on the interaction of the variables, and cancelling out the effects of omitted variables and omitted MA terms through averaging. The proposed procedure is applied to simulated data from various forms of model misspecifications. The forecasting accuracy of the proposed procedure was compared to an automatically selected equal lag-length VAR. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and MA terms are omitted, the proposed procedure is better in forecasting than the automatically selected equal lag-length VAR model.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 55902.

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Date of creation: 2014
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Handle: RePEc:pra:mprapa:55902

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Keywords: BACE; AVAR; Robustness Procedures;

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  1. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
  2. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," Working Papers 634, Queen Mary, University of London, School of Economics and Finance.
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  8. Hsiao, Cheng, 1981. "Autoregressive modelling and money-income causality detection," Journal of Monetary Economics, Elsevier, vol. 7(1), pages 85-106.
  9. Dimitris Korobilis, 2010. "VAR Forecasting Using Bayesian Variable Selection," Working Paper Series 51_10, The Rimini Centre for Economic Analysis, revised Apr 2011.
  10. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
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  14. Lutkepohl, Helmut, 2006. "Forecasting with VARMA Models," Handbook of Economic Forecasting, Elsevier.
  15. Keating, John W., 2000. "Macroeconomic Modeling with Asymmetric Vector Autoregressions," Journal of Macroeconomics, Elsevier, vol. 22(1), pages 1-28, January.
  16. David E. Runkle, 1987. "Vector autoregressions and reality," Staff Report 107, Federal Reserve Bank of Minneapolis.
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  18. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-42, October.
  19. Fackler, James S & Krieger, Sandra C, 1986. "An Application of Vector Time Series Techniques to Macroeconomic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 71-80, January.
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