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Fractional Bayesian Lag Length Inference in Multivariate Autoregressive Processes

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  • Mattias Villani

Abstract

The posterior distribution of the number of lags in a multivariate autoregression is derived under an improper prior for the model parameters. The fractional Bayes approach is used to handle the indeterminacy in the model selection caused by the improper prior. An asymptotic equivalence between the fractional approach and the Schwarz Bayesian Criterion (SBC) is proved. Several priors and three loss functions are entertained in a simulation study which focuses on the choice of lag length. The fractional Bayes approach performs very well compared to the three most widely used information criteria, and it seems to be reasonably robust to changes in the prior distribution for the lag length, especially under the zero‐one loss.

Suggested Citation

  • Mattias Villani, 2001. "Fractional Bayesian Lag Length Inference in Multivariate Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(1), pages 67-86, January.
  • Handle: RePEc:bla:jtsera:v:22:y:2001:i:1:p:67-86
    DOI: 10.1111/1467-9892.00212
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    Cited by:

    1. Ming-Liang Yeh & Hsiao-Ping Chu & Peter Sher & Yi-Chia Chiu, 2010. "R&D intensity, firm performance and the identification of the threshold: fresh evidence from the panel threshold regression model," Applied Economics, Taylor & Francis Journals, vol. 42(3), pages 389-401.
    2. Warne, Anders, 2006. "Bayesian inference in cointegrated VAR models: with applications to the demand for euro area M3," Working Paper Series 692, European Central Bank.
    3. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

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