Abstract
SummaryThis paper introduces measures for how each moment contributes to the precision of parameter estimates in generalized method of moments settings. For example, one of the measures asks what would happen to the variance of the parameter estimates if a particular moment was dropped from the estimation. The measures are all easy to compute. We illustrate the usefulness of the measures through two simple examples as well as an application to a model of joint retirement planning of couples. We estimate the model using the British Household Panel Survey, and we find evidence of complementarities in leisure. Our sensitivity measures illustrate that the estimate of the complementarity is primarily informed by the distribution of differences in planned retirement dates. The estimated econometric model can be interpreted as a bivariate ordered‐choice model that allows for simultaneity. This makes the model potentially useful in other applications.
Highlights
Indirect inference and other nonlinear generalized method of moments (GMM) estimators are used extensively in empirical research
While we focus on the precision of the parameter estimates, more recently Armstrong and Kolesár (2018) and Bonhomme and Weidner (2018) studied local misspecification
While some of the measures below apply to just-identified models, we focus here on overidentified models where the number of moments are larger than the number of parameters in θ, and the weighting matrix plays a role
Summary
This paper introduces measures for how each moment contributes to the precision of parameter estimates in generalized method of moments settings. One of the measures asks what would happen to the variance of the parameter estimates if a particular moment was dropped from the estimation. We illustrate the usefulness of the measures through two simple examples as well as an application to a model of joint retirement planning of couples. Our sensitivity measures illustrate that the estimate of the complementarity is primarily informed by the distribution of differences in planned retirement dates. The estimated econometric model can be interpreted as a bivariate ordered-choice model that allows for simultaneity. This makes the model potentially useful in other applications
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