Walter Sosa-Escudero

Learn More
Considerable effort has been exercised in estimating mean returns to education while carefully considering biases arising from unmeasured ability and measurement error. Recent work has investigated whether there are variations from the “mean” return to education across the population with mixed results. We use an instrumental variables estimator for(More)
We use recent unconditional quantile regression methods (UQR) to study the distributive effects of education in Argentina. Standard methods usually focus on mean effects, or explore distributive effects by either making stringent modeling assumptions, and/or through counterfactual decompositions that require several temporal observations. An empirical case(More)
Typical panel data models make use of the assumption that the regression parameters are the same for each individual cross sectional unit. We propose tests for slope heterogeneity in panel data models. Our tests are based on the conditional Gaussian likelihood function in order to avoid the incidental parameters problem induced by the inclusion of(More)
his paper, using data from the British Household Panel Survey, examines the effect of employer-specific human capital on a wage equation model. The analysis provides evidence that there exists only a modest tenure effect, which after controlling for endogeneity bias does not exceed the 8% (10-years effect). Furthermore, estimates based on two-stage quantile(More)
This paper derives tests for skewness and kurtosis for the one-way error components model. The test statistics are based on the between and within transformations of pooled OLS residuals, and are derived in a conditional moments framework. We derive the limiting distribution of the test statistics for panels with large cross-sectional and fixed time-series(More)
This paper proposes a set of moment conditions for the estimation of linear dynamic panel data models. In the spirit of Chamberlain’s (1982, 1984) approach, these conditions arise from parameterizing the relationship between covariates and unobserved time invariant effects. A GMM framework is used to derive an optimal estimator, with no efficiency loss(More)
  • 1