Working Paper: NBER ID: w14411
Authors: Anthony W. Lynch; Jessica A. Wachter
Abstract: 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.
Keywords: Generalized Method of Moments; Unequal Sample Lengths; Financial Econometrics
JEL Codes: C32; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| adjusted-moment estimator (C51) | more efficient than standard GMM (C51) |
| overidentified estimator (C51) | more efficient than standard GMM (C51) |
| using full sample (C83) | better estimates (C51) |
| full sample (Y60) | affects point estimates and standard errors (C20) |
| including additional data (Y10) | more precise estimates (C13) |
| inclusion of data from earlier periods (N00) | impacts estimation of predictive regressions (C51) |
| correlation between returns in different markets (G15) | improved estimates of predictability (C53) |