Estimation and inference in semiparametric quantile factor models
Estimation and inference in semiparametric quantile factor models
复制标题
半参数分位数因子模型中的估计和推断
DOI：
10.1016/j.jeconom.2020.07.003
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发表时间：
2020
影响因子：
6.3
通讯作者：
Gao, Jiti
中科院分区：
文献类型：
--
作者：
Ma, Shujie;Linton, Oliver;Gao, Jiti
我们考虑了一个半参数分位数因子面板模型，该模型允许观察到的股票特定特征以非线性时变的方式影响股票收益，将康纳，哈格曼和林顿（2012）扩展到分位数限制的情况。我们提出了一个基于筛子的估计方法，很容易实现。我们提供的工具是强大的存在的时刻和弱横截面依赖的特殊误差项的形式的推理。我们将我们的方法应用于每日股票收益率数据，我们发现许多特征暴露曲线中存在显著的非线性证据。
We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. We apply our method to daily stock return data where we find significant evidence of nonlinearity in many of the characteristic exposure curves.