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Semiparametric Estimation and Variable Selection for Single-index Copula Models

Author

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  • Bingduo Yang
  • Christian M. Hafner
  • Guannan Liu
  • Wei Long
Abstract
A copula with a flexibly dependence structure can capture complexity and heterogeneity in economic and financial time series. Based on the recently proposed single‐index copula, we propose a simultaneous variable selection and estimation procedure. This method allows for choosing the most relevant state variables by using a penalized estimation with large sample properties derived. Simulation results demonstrate the good performance of the method in selecting relevant state variables and estimating unknown index coefficients and dependence parameters. We apply the proposed procedure to four states' housing markets in the United States and identify six macroeconomic factors that drive their dependence structure.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Bingduo Yang & Christian M. Hafner & Guannan Liu & Wei Long, 2019. "Semiparametric Estimation and Variable Selection for Single-index Copula Models," Working Papers 2019-07-05, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    • Handle: RePEc:wyi:wpaper:002440
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    3. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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