Time-Series Estimation of the Effects of Natural Experiments

This paper builds on the labor econometrics and classical treatment effects literatures to provide a framework supporting causal concepts and methods for estimating effects of natural experiments operating over time in an explicitly dynamic time-series context. We examine conditions for the construction of covariates instrumental in identifying effects of interest that lead to new tests for unconfoundedness, a key condition for the identification of causal effects that we link to the concept of Granger non-causality. Our new tests for unconfoundedness are useful in both cross-section and dynamic time-series settings. Acknowledgments: The author is grateful for the comments and suggestions of the editor, two anonymous referees, Douglas Bernheim, Karim Chalak, Xiaohong Chen, Clive Granger, Jerry Hausman, David Hendry, Jinyong Han, Kei Hirano, Paul Johnson, Qi Li, Robert Marshall, Jack Porter, Chris Stomberg, Timo Teräsvirta, Aman Ullah, participants of the San Diego Predictive Methodology and Application in Economics and Finance Conference in Honor of Clive W.J. Granger, and participants in my seminars at the Stockholm School of Economics in May and December, 2003. All errors and omissions are the author’s responsibility. The author thanks Michael J. Bacci and Tolga Cenesizoglu for their invaluable assistance with the preparation