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Simulation-Based Density Estimation for Time Series Using Covariate Data

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  • Yin Liao
  • John Stachurski

Abstract

This article proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.

Suggested Citation

  • Yin Liao & John Stachurski, 2015. "Simulation-Based Density Estimation for Time Series Using Covariate Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 595-606, October.
  • Handle: RePEc:taf:jnlbes:v:33:y:2015:i:4:p:595-606
    DOI: 10.1080/07350015.2014.982247
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    File URL: http://hdl.handle.net/10.1080/07350015.2014.982247
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    Cited by:

    1. Li, Shuo & Tu, Yundong, 2016. "n-consistent density estimation in semiparametric regression models," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 91-109.

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