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Reduced-rank time-varying vector autoregressions

Author

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  • Joris de Wind
  • Luca Gambetti

Abstract

The standard time-varying VAR workhorse suffers from overparameterization, which is a serious problem as it limits the number of variables and lags that can be incorporated in the model. Read also: CPB Discussion Paper 271 'Time variation in the dynamic effects of unanticipated changes in tax policy'. As a solution for the overparameterization problem, we propose a new, more parsimonious time-varying VAR model setup with which we can reliably estimate larger models including more variables and/or more lags than was possible until now. The new model setup implies cross-equation restrictions on the time variation that are empirically supported, theoretically appealing, and make the Bayesian estimation procedure much faster.

Suggested Citation

  • Joris de Wind & Luca Gambetti, 2014. "Reduced-rank time-varying vector autoregressions," CPB Discussion Paper 270, CPB Netherlands Bureau for Economic Policy Analysis.
    • Handle: RePEc:cpb:discus:270
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    References listed on IDEAS

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    10. Hofmann, Boris & Peersman, Gert & Straub, Roland, 2012. "Time variation in U.S. wage dynamics," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 769-783.
    11. Joris de Wind, 2014. "Time variation in the dynamic effects of unanticipated changes in tax policy," CPB Discussion Paper 271.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
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    Cited by:

    1. Joris de Wind, 2014. "Time variation in the dynamic effects of unanticipated changes in tax policy," CPB Discussion Paper 271, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Francesco Corsello & Valerio Nispi Landi, 2020. "Labor Market and Financial Shocks: A Time‐Varying Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 777-801, June.
    3. Andrew Binning & Junior Maih, 2015. "Applying Flexible Parameter Restrictions in Markov-Switching Vector Autoregression Models," Working Papers No 12/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Marta Banbura & Andries van Vlodrop, 2018. "Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean," Tinbergen Institute Discussion Papers 18-025/IV, Tinbergen Institute.
    5. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    6. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Simon Beyeler, 2019. "Streamlining Time-varying VAR with a Factor Structure in the Parameters," Working Papers 19.03, Swiss National Bank, Study Center Gerzensee.
    8. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    9. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    10. Joris de Wind, 2014. "Time variation in the dynamic effects of unanticipated changes in tax policy," CPB Discussion Paper 271.rdf, CPB Netherlands Bureau for Economic Policy Analysis.

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    More about this item

    JEL classification:

    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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