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Dynamic Vector Mode Regression

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  • Gordon C. R. Kemp
  • Paulo M. D. C. Parente
  • J. M. C. Santos Silva

Abstract

We study the semiparametric estimation of the conditional mode of a random vector that has a continuous conditional joint density with a well-defined global mode. A novel full-system estimator is proposed and its asymptotic properties are studied. We specifically consider the estimation of vector autoregressive conditional mode models and of systems of linear simultaneous equations defined by mode restrictions. The proposed estimator is easy to implement and simulations suggest that it is reasonably behaved in finite samples. An empirical example illustrates the application of the proposed methods, including its use to obtain multistep forecasts and to construct impulse response functions.

Suggested Citation

  • Gordon C. R. Kemp & Paulo M. D. C. Parente & J. M. C. Santos Silva, 2020. "Dynamic Vector Mode Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 647-661, July.
    • Handle: RePEc:taf:jnlbes:v:38:y:2020:i:3:p:647-661
    DOI: 10.1080/07350015.2018.1562935
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    References listed on IDEAS

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    Cited by:

    1. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.
    2. Eduardo Schirmer Finn & Eduardo Horta, 2024. "Convolution Mode Regression," Papers 2412.05736, arXiv.org.
    3. Ullah, Aman & Wang, Tao & Yao, Weixin, 2023. "Semiparametric partially linear varying coefficient modal regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 1001-1026.
    4. Venables, Anthony J., 2017. "Breaking into tradables: Urban form and urban function in a developing city," Journal of Urban Economics, Elsevier, vol. 98(C), pages 88-97.
    5. Tao Wang, 2024. "Non‐parametric Estimator for Conditional Mode with Parametric Features," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 44-73, February.
    6. Aman Ullah & Tao Wang & Weixin Yao, 2022. "Nonlinear modal regression for dependent data with application for predicting COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1424-1453, July.
    7. Zhe Sun & Yundong Tu, 2024. "Factors in Fashion: Factor Analysis towards the Mode," Papers 2409.19287, arXiv.org.

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