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Truncated priors for tempered hierarchical Dirichlet process vector autoregression

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  • Sergei Seleznev

    (Bank of Russia, Russian Federation)

Abstract

We construct priors for the tempered hierarchical Dirichlet process vector autoregression model (tHDP-VAR) that in practice do not lead to explosive forecasting dynamics. Additionally, we show that tHDP-VAR and its variational Bayesian approximation with heuristics demonstrate competitive or even better forecasting performance on US and Russian datasets.

Suggested Citation

  • Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps47
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian nonparametrics; forecasting; hierarchical Dirichlet process; infinite hidden Markov model.;
    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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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