Search Results
Showing results 1 to 10 of approximately 11.
(refine search)
Working Paper
Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification
This paper studies semiparametric versions of the classical sample selection model (Heckman (1976, 1979)) without exclusion restrictions. We extend the analysis in Honoré and Hu (2020) by allowing for parameter heterogeneity and derive implications of this model. We also consider models that allow for heteroskedasticity and briefly discuss other extensions. The key ideas are illustrated in a simple wage regression for females. We find that the derived implications of a semiparametric version of Heckman's classical sample selection model are consistent with the data for women with no college ...
Working Paper
The COVID-19 Pandemic and Asian American Employment
Recent studies have documented the disparate impact of the COVID-19 pandemic on labor market outcomes for different racial groups. This paper adds to this literature by documenting that the employment of Asian Americans - in particular those with no college education - has been especially hard hit by the economic crisis associated with the onset of the pandemic. This can only partly be explained by differences in demographics, local market conditions, and job characteristics, and it also cannot be entirely explained by possible different selection into education levels across ethnic groups. ...
Working Paper
Poor (Wo)man’s Bootstrap
The bootstrap is a convenient tool for calculating standard errors of the parameters of complicated econometric models. Unfortunately, the fact that these models are complicated often makes the bootstrap extremely slow or even practically infeasible. This paper proposes an alternative to the bootstrap that relies only on the estimation of one-dimensional parameters. The paper contains no new difficult math. But we believe that it can be useful.
Working Paper
Estimation of panel data regression models with two-sided censoring or truncation
This paper constructs estimators for panel data regression models with individual specific heterogeneity and two-sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re-censored or re-truncated residuals. While these moment conditions do not identify the parameter of interest, they can be used to motivate objective functions that do. We apply one of the estimators to study the effect of a Danish tax reform on household portfolio choice. The idea behind the estimators can also be used in a cross sectional setting.
Working Paper
Easy Bootstrap-Like Estimation of Asymptotic Variances
The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the bootstrap can be very time-consuming. In a recent paper, Honor and Hu (2017), we propose a ?Poor (Wo)man's Bootstrap? based on one-dimensional estimators. In this paper, we propose a modified, simpler method and illustrate its potential for estimating asymptotic variances.
Working Paper
Simultaneity in Binary Outcome Models with an Application to Employment for Couples
Two of Peter Schmidt’s many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this ...
Working Paper
Estimation of a transformation model with truncation, interval observation and time-varying covariates
Abrevaya (1999b) considered estimation of a transformation model in the presence of left-truncation. This paper observes that a cross-sectional version of the statistical model considered in Frederiksen, Honor, and Hu (2007) is a generalization of the model considered by Abrevaya (1999b) and the generalized model can be estimated by a pairwise comparison version of one of the estimators in Frederiksen, Honor, and Hu (2007). Specifically, our generalization will allow for discretized observations of the dependent variable and for piecewise constant time- varying explanatory variables.
Working Paper
The COVID-19 Pandemic and Asian American Employment
This paper documents that the employment of Asian Americans with no college education has been especially hard hit by the economic crisis associated with the Covid-19 pandemic. This cannot be explained by differences in demographics or in job characteristics. Asian American employment is also harder hit unconditional on education. This suggests that different selection into education levels across ethnic groups alone cannot explain the main results. This pattern does not apply to the 2008 economic crisis. Our findings suggest that this period might be fundamentally different from the previous ...
Working Paper
The COVID-19 Pandemic and Asian American Employment
This paper documents that the employment of Asian Americans with no college education has been especially hard hit by the economic crisis associated with the Covid-19 pandemic. This cannot be explained by differences in demographics or in job characteristics, and the pattern does not apply to the 2008 economic crisis. We find some evidence that the effect is larger in occupations with more interpersonal tasks. Asian American employment is also harder hit unconditional on education. This suggests that different selection into education levels across ethnic groups alone cannot explain the main ...
Working Paper
Selection Without Exclusion
It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. We find that the additional structure in the classical sample selection model can significantly reduce the identified set for the parameters ...