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Time-Varying Local Projections

Author

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  • Germano Ruisi

    (Queen Mary University of London)

Abstract

In recent years local projections have become a more and more popular methodology for the estimation of impulse responses. Besides being relatively easy to implement, the main strength of this approach relative to the traditional VAR one is that there is no need to impose any specific assumption on the dynamics of the data. This paper models local projections in a time-varying framework and provides a Gibbs sampler routine to estimate them. A simulation study shows how the performance of the algorithm is satisfactory while the usefulness of the model developed here is shown through an application to fiscal policy shocks.

Suggested Citation

  • Germano Ruisi, 2019. "Time-Varying Local Projections," Working Papers 891, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:891
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2019/wp891.pdf
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    References listed on IDEAS

    as
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    10. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    11. Ulrich K. Müller, 2013. "Risk of Bayesian Inference in Misspecified Models, and the Sandwich Covariance Matrix," Econometrica, Econometric Society, vol. 81(5), pages 1805-1849, September.
    12. Pascal Paul, 2020. "The Time-Varying Effect of Monetary Policy on Asset Prices," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 690-704, October.
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    Cited by:

    1. Riccardo Lucchetti & Francesco Valentini, 2023. "Kernel-based time-varying IV estimation: handle with care," Empirical Economics, Springer, vol. 65(6), pages 3001-3026, December.
    2. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Apr 2023.

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    More about this item

    Keywords

    Time-Varying Coefficients; Local Projections;

    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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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