Testing conditional independence in casual inference for time series data

Author:

Cai Zongwu1ORCID,Fang Ying234,Lin Ming234,Tang Shengfang5

Affiliation:

1. Department of Economics University of Kansas Lawrence Kansas USA

2. Wang Yanan Institute for Studies in Economics and Fujian Key Laboratory of Statistical Sciences Xiamen University Xiamen Fujian China

3. Department of Statistics and Data Science, School of Economics Xiamen University Xiamen Fujian China

4. Laboratory of Digital Finance Xiamen University Xiamen Fujian China

5. School of Finance Jiangxi University of Finance and Economics Nanchang Jiangxi China

Abstract

In this paper, we propose a new procedure to test conditional independence assumption in studying casual inference for time series data. The conditional independence assumption is transformed to a nonparametric conditional moment test with the help of auxiliary variables which are allowed to affect policy choice but the dependence can be fully captured by potential outcomes and observable controls. When the policy choice is binary, a nonparametric statistic test is developed further for testing the conditional independence assumption conditional on policy propensity score. Under some regular conditions, we show that the proposed test statistics are asymptotically normal under the null hypotheses for time series data. In addition, the performances of the proposed methods are illustrated through Monte Carlo simulations and a real example considered in Angrist and Kuersteiner (2011).

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

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