Abstract
When genuine panel data samples are not available, repeated cross-sectional surveys can be used to form so-called pseudo panels. In this article, we investigate the properties of linear pseudo panel data estimators with fixed number of cohorts and time observations. We extend standard linear pseudo panel data setup to models with factor residuals by adapting the quasi-differencing approach developed for genuine panels. In a Monte Carlo study, we find that the proposed procedure has good finite sample properties in situations with endogeneity, cohort interactive effects, and near nonidentification. Finally, as an illustration the proposed method is applied to data from Ecuador to study labor supply elasticity. Supplementary materials for this article are available online.
Highlights
Over the last three decades, panel data techniques proved to be of high value for both micro and macro economists
The GMMl0 estimator is severely biased in finite samples, driving the corresponding values of the root mean square estimation (RMSE)
As thoroughly discussed by McKenzie (2004) and Verbeek (2008), different types of asymptotic approximations are available for pseudo panel data models, depending on their dimensions
Summary
Over the last three decades, panel data techniques proved to be of high value for both micro and macro economists. Existing estimation methods for linear pseudo panel data models assume that the unobserved individual heterogeneity can be properly captured using the standard additive error component structure. The key component of pseudo panel analysis is the use of cohort-based data in estimation. We introduce a factor structure to linear pseudo panel data models with a fixed number of time periods and cohorts. A well-known implication (see, e.g., Inoue 2008) of the Type I asymptotics is the robustness of the estimator based on crosssectional averages to the presence of endogenous explanatory variables. Discussed later in this article, robustness to endogeneity is only achieved under the assumption of strong identification Another implication for our analysis is that under Type I (unlike Type II) asymptotics, the estimator that is discussed in this article does not suffer from the “many instrument” bias as in Bekker (1994) and Bekker and van der Ploeg (2005). The intuition behind these properties is discussed later in the article
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