Using Samples of Unequal Length in Generalized Method of Moments Estimation
Many applications in financial economics use data series with different starting or ending dates. This paper describes estimation methods, based on the generalized method of moments (GMM), which make use of all available data for each moment condition. We introduce two asymptotically equivalent estimators that are consistent, asymptotically normal, and more efficient asymptotically than standard GMM. We apply these methods to estimating predictive regressions in international data and show that the use of the full sample affects point estimates and standard errors for both assets with data available for the full period and assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.
We thank Yacine Ait-Sahalia, David Chapman, Robert Engle, Martin Lettau, Andrew Lo, Kenneth Singleton, Robert Stambaugh, Jim Stock, Amir Yaron, Motohiro Yogo, as well as seminar participants at the 2005 AFA meetings, at New York University, at the Wharton School and at the University of Pennsylvania Department of Economics for their comments and suggestions. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
Lynch, Anthony W. & Wachter, Jessica A., 2013. "Using Samples of Unequal Length in Generalized Method of Moments Estimation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(01), pages 277-307, February. citation courtesy of