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Bayesian analysis of seasonally cointegrated VAR models

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  • Wróblewska, Justyna

Abstract

The aim is to develop a Bayesian seasonally cointegrated model for quarterly data. Relevant prior structure is proposed, and the set of full conditional posterior distributions is derived, enabling us to employ the Gibbs sampler for posterior inference. The identification of cointegrating spaces is obtained by orthonormality restrictions imposed on vectors spanning them. The point estimation of the cointegrating spaces is also discussed. In the presence of a seasonal pattern with one cycle per year, the cointegrating vectors belong to the complex space, which should be taken into account in the identification scheme. The methodology is illustrated by the analysis of money and prices in the Polish economy.

Suggested Citation

  • Wróblewska, Justyna, 2025. "Bayesian analysis of seasonally cointegrated VAR models," Econometrics and Statistics, Elsevier, vol. 35(C), pages 55-70.
    • Handle: RePEc:eee:ecosta:v:35:y:2025:i:c:p:55-70
    DOI: 10.1016/j.ecosta.2023.02.002
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    More about this item

    Keywords

    Seasonal cointegration; Reduced rank regression; Error correction model; Bayesian model comparison;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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