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Kernel-based Time-Varying IV estimation: handle with care

Author

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  • Lucchetti, Riccardo
  • Valentini, Francesco

Abstract

Giraitis, Kapetanios, and Marcellino (Journal of Econometrics, 2020) proposed a kernel-based time-varying coefficients IV estimator. By using entirely different code, We broadly replicate the simulation results and the empirical application on the Phillips Curve but we note that a small coding mistake might have affected some of the reported results. Further, we extend the results by using a different sample and many kernel functions; we find that the estimator is remarkably robust across a wide range of smoothing choices, but the effect of outliers may be less obvious than expected.

Suggested Citation

  • Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:110033
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    References listed on IDEAS

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    1. Giraitis, Liudas & Kapetanios, George & Marcellino, Massimiliano, 2021. "Time-varying instrumental variable estimation," Journal of Econometrics, Elsevier, vol. 224(2), pages 394-415.
    2. Germano Ruisi, 2019. "Time-Varying Local Projections," Working Papers 891, Queen Mary University of London, School of Economics and Finance.
    3. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    4. Jushan Bai & Pierre Perron, 2003. "Critical values for multiple structural change tests," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 72-78, June.
    5. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    6. Kalaba, Robert & Rasakhoo, Nima & Tesfatsion, Leigh, 1989. "A FORTRAN program for time-varying linear regression via flexible least squares," Computational Statistics & Data Analysis, Elsevier, vol. 7(3), pages 291-309, February.
    7. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    9. Giraitis, L. & Kapetanios, G. & Yates, T., 2014. "Inference on stochastic time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 179(1), pages 46-65.
    10. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    11. Liudas Giraitis & George Kapetanios & Tony Yates, 2018. "Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 129-149, March.
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    Cited by:

    1. Michele Fratianni & Federico Giri & Riccardo Lucchetti & Francesco Valentini, 2022. "Monetization, wars, and the Italian fiscal multiplier," Mo.Fi.R. Working Papers 176, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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