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A new algorithm for structural restrictions in Bayesian vector autoregressions

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  • Korobilis, Dimitris

Abstract

A comprehensive methodology for inference in vector autoregressions (VARs) using sign and other structural restrictions is developed. The reduced-form VAR disturbances are driven by a few common factors and structural identification restrictions can be incorporated in their loadings in the form of parametric restrictions. A Gibbs sampler is derived that allows for reduced-form parameters and structural restrictions to be sampled efficiently in one step. A key benefit of the proposed approach is that it allows for treating parameter estimation and structural inference as a joint problem. An additional benefit is that the methodology can scale to large VARs with multiple shocks, and it can be extended to accommodate non-linearities, asymmetries, and numerous other interesting empirical features. The excellent properties of the new algorithm for inference are explored using synthetic data experiments, and by revisiting the role of financial factors in economic fluctuations using identification based on sign restrictions.

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  • Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:eecrev:v:148:y:2022:i:c:s001429212200143x
    DOI: 10.1016/j.euroecorev.2022.104241
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    Cited by:

    1. Dimitris Korobilis & Maximilian Schroder, 2023. "Monitoring multicountry macroeconomic risk," Papers 2305.09563, arXiv.org.
    2. Josué Diwambuena & Francesco Ravazzolo, 2022. "What are the drivers of Labor Productivity?," BEMPS - Bozen Economics & Management Paper Series BEMPS86, Faculty of Economics and Management at the Free University of Bozen.
    3. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Bayesian Modeling of Time-Varying Parameters Using Regression Trees," Working Papers 23-05, Federal Reserve Bank of Cleveland.
    4. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2023. "Agreed and Disagreed Uncertainty," Working Paper series 23-01, Rimini Centre for Economic Analysis.
    5. Hilde C. Bjørnland & Malin C. Jensen & Leif Anders Thorsrud, 2023. "Business Cycle and Health Dynamics during the COVID-19 Pandemic. A Scandinavian Perspective," Working Papers No 15/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2024. "Shocked to the core: a new model to understand euro area inflation," Research Bulletin, European Central Bank, vol. 117.
    7. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    8. Ciccarelli, Matteo & Darracq Pariès, Matthieu & Priftis, Romanos & Angelini, Elena & Bańbura, Marta & Bokan, Nikola & Fagan, Gabriel & Gumiel, José Emilio & Kornprobst, Antoine & Lalik, Magdalena & Mo, 2024. "ECB macroeconometric models for forecasting and policy analysis," Occasional Paper Series 344, European Central Bank.

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    More about this item

    Keywords

    Gibbs sampling; Factor model decomposition; Large VAR; Sign restrictions;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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