Testing Conditional Independence in Macroeconomic Policy Evaluation for Time Series Data

In this paper, we propose a new procedure to test conditional independence assumption for macroeconomic policy evaluation in a time series context. The unconfoundedness assumption is transformed to a nonparametric conditional moment test using auxiliary variables which are allowed to affect potential outcomes 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 unconfoundedness assumption conditional on policy propensity score. The proposed test statistics are shown to have the limiting normal distribution under the null hypotheses for time series data. Monte Carlo simulations are conducted to examine the finite sample performances of the proposed test statistics. Finally, the proposed test method is applied to testing the conditional unconfoundedness in a real example as considered in Angrist and Kuersteiner (2011).