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Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints

Author

Listed:
  • Joshua C. C. Chan
  • Davide Pettenuzzo
  • Aubrey Poon
  • Dan Zhu

Abstract

Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large Vector Autoregressions or when multiple linear equality and inequality constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from linear equality and inequality constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian Vector Autoregression where we simultaneously impose a mix of linear equality and inequality constraints on the future trajectories of key US macroeconomic indicators over the 2020--2022 period.

Suggested Citation

  • Joshua C. C. Chan & Davide Pettenuzzo & Aubrey Poon & Dan Zhu, 2024. "Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints," Papers 2407.02262, arXiv.org.
  • Handle: RePEc:arx:papers:2407.02262
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    References listed on IDEAS

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    1. Joshua C. C. Chan & Michael Pfarrhofer, 2025. "Large Bayesian VARs for Binary and Censored Variables," Papers 2506.01422, arXiv.org.

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    More about this item

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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