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Inference on stochastic time-varying coefficient models

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  • Giraitis, L.
  • Kapetanios, G.
  • Yates, T.

Abstract

Recently, there has been considerable work on stochastic time-varying coefficient models as vehicles for modelling structural change in the macroeconomy with a focus on the estimation of the unobserved paths of random coefficient processes. The dominant estimation methods, in this context, are based on various filters, such as the Kalman filter, that are applicable when the models are cast in state space representations. This paper introduces a new class of autoregressive bounded processes that decompose a time series into a persistent random attractor, a time varying autoregressive component, and martingale difference errors. The paper examines, rigorously, alternative kernel based, nonparametric estimation approaches for such models and derives their basic properties. These estimators have long been studied in the context of deterministic structural change, but their use in the presence of stochastic time variation is novel. The proposed inference methods have desirable properties such as consistency and asymptotic normality and allow a tractable studentization. In extensive Monte Carlo and empirical studies, we find that the methods exhibit very good small sample properties and can shed light on important empirical issues such as the evolution of inflation persistence and the purchasing power parity (PPP) hypothesis.

Suggested Citation

  • Giraitis, L. & Kapetanios, G. & Yates, T., 2014. "Inference on stochastic time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 179(1), pages 46-65.
  • Handle: RePEc:eee:econom:v:179:y:2014:i:1:p:46-65
    DOI: 10.1016/j.jeconom.2013.10.009
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    References listed on IDEAS

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    Cited by:

    1. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    2. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    3. Hongjun Li & Zhongjian Lin & Cheng Hsiao, 2015. "Testing purchasing power parity hypothesis: a semiparametric varying coefficient approach," Empirical Economics, Springer, vol. 48(1), pages 427-438, February.
    4. Mogens Fosgerau & Jinwon Kim & Abhishek Ranjan, 2017. "Vickrey Meets Alonso: Commute Scheduling and Congestion in a Monocentric City," Discussion Papers 17-25, University of Copenhagen. Department of Economics.
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    7. Richard T. Baillie & Fabio Calonaci & George Kapetanios, 2019. "Hierarchical Time Varying Estimation of a Multi Factor Asset Pricing Model," Working Papers 879, Queen Mary University of London, School of Economics and Finance.
    8. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    9. Jensen, Henrik & Petrella, Ivan & Ravn, S�ren Hove & Santoro, Emiliano, 2017. "Leverage and Deepening Business Cycle Skewness," CEPR Discussion Papers 12239, C.E.P.R. Discussion Papers.
    10. Richard T. Baillie & Dooyeon Cho, 2016. "Assessing Euro Crises from a Time Varying International CAPM Approach," Working Paper series 16-03, Rimini Centre for Economic Analysis.
    11. Kapetanios, George & Millard, Stephen & Price, Simon & Petrova, Katerina, 2018. "Time varying cointegration and the UK Great Ratios," Essex Finance Centre Working Papers 23320, University of Essex, Essex Business School.
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    16. Mathias Klein & Ludger Linnemann, 2018. "Macroeconomic Effects of Government Spending: The Great Recession Was (Really) Different," Discussion Papers of DIW Berlin 1754, DIW Berlin, German Institute for Economic Research.
    17. Baillie, Richard T. & Cho, Dooyeon, 2014. "Time variation in the standard forward premium regression: Some new models and tests," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 52-63.
    18. Li, Dong & Zhang, Xingfa & Zhu, Ke & Ling, Shiqing, 2018. "The ZD-GARCH model: A new way to study heteroscedasticity," Journal of Econometrics, Elsevier, vol. 202(1), pages 1-17.
    19. Chang, Yoosoon & Kwak, Boreum, 2017. "U.S. monetary-fiscal regime changes in the presence of endogenous feedback in policy rules," IWH Discussion Papers 15/2017, Halle Institute for Economic Research (IWH).
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    23. Liu, Laura & Matthes, Christian & Petrova, Katerina, 2018. "Monetary Policy across Space and Time," Working Paper 18-14, Federal Reserve Bank of Richmond.

    More about this item

    Keywords

    Time-varying coefficient models; Random coefficient models; Nonparametric estimation; Kernel estimation; Autoregressive processes;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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