Semiparametric estimation and variable selection for single-index copula models

Bingduo Yang, Christian Hafner, Guannan Liu and Wei Long

No 2022011, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

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.

Keywords: Semiparametric Copula; Single-Index Copula; Variable Selection; SCAD (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Pages: 41
Date: 2022-02-01
Note: In: Journal of Applied Econometrics, 2021, vol. 36(7), p. 962-988
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Related works:
Journal Article: Semiparametric estimation and variable selection for single‐index copula models (2021)
Working Paper: Semiparametric Estimation and Variable Selection for Single-index Copula Models (2019)
Working Paper: Semiparametric Estimation and Variable Selection for Single-index Copula Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2022011

DOI: 10.1002/jae.2812

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