Semiparametric Estimation and Variable Selection for Single‐Index Copula Models
Semiparametric Estimation and Variable Selection for Single‐Index Copula Models
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DOI：
10.1002/jae.2812
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发表时间：
2021-03
影响因子：
2.1
通讯作者：
Bingduo Yang;C. Hafner;Guannan Liu;Wei Long
中科院分区：
文献类型：
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作者：
Bingduo Yang;C. Hafner;Guannan Liu;Wei Long
具有灵活相关结构的Copula模型可以用来捕捉经济和金融时间序列的复杂性和异质性。然而，对于使用Copulas的规范过程，几乎没有方法论指导。本文通过考虑最近提出的单指数Copula来填补这一空白，我们为其提出了一种同时估计和变量选择的过程。所提出的方法允许使用惩罚估计从综合集合中选择最相关的状态变量，并推导出其大样本性质。仿真结果表明，该方法在选择合适的状态变量、估计未知指标系数和相关参数方面具有良好的性能。新程序的应用确定了美国房地产市场依赖的六个宏观经济驱动因素。
A copula model with flexibly specified dependence structure can be useful to capture the complexity and heterogeneity in economic and financial time series. However, there exists little methodological guidance for the specification process using copulas. This paper contributes to fill this gap by considering the recently proposed single-index copulas, for which we propose a simultaneous estimation and variable selection procedure. The proposed method allows to choose the most relevant state variables from a comprehensive set using a penalized estimation, and we derive its large sample properties. Simulation results demonstrate the good performance of the proposed method in selecting the appropriate state variables and estimating the unknown index coefficients and dependence parameters. An application of the new procedure identifies six macroeconomic driving factors for the dependence among U.S. housing markets.