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

Author

Listed:
  • 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.

Suggested Citation

  • Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:55902
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    References listed on IDEAS

    as
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    Cited by:

    1. Atif Maqbool Khan & Jacek Kwiatkowski & Magdalena Osińska & Marcin Błażejowski, 2021. "Factors of Renewable Energy Consumption in the European Countries—The Bayesian Averaging Classical Estimates Approach," Energies, MDPI, vol. 14(22), pages 1-24, November.
    2. Marcin Blazejowski & Jacek Kwiatkowski, 2018. "Bayesian Averaging of Classical Estimates (BACE) for gretl," gretl working papers 6, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.

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    More about this item

    Keywords

    BACE; AVAR; Robustness Procedures;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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