Volume 3, Issue 1

This paper constructs estimators for panel data regression models with individual specific heterogeneity and two-sided censoring and truncation. Following Powell 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.

We suggest a pragmatic extension of the non-parametric copula-graphic estimator to a depending competing risks model with covariates. Our model is an attractive empirical approach for practitioners in many disciplines as it does not require knowledge of the marginal distributions. Although non-observable and only set-identifiable in most applications, classical duration models typically impose ad-hoc assumptions on their functional forms. Instead of directly estimating these distributions, we suggest a plug-in regression framework which utilises an estimator for the observable cumulative incidence curves which specification can be visually inspected. We perform simulations and estimate an unemployment duration model to demonstrate the advantages of our model compared to classical duration models such as the Cox proportional hazard model.

This paper proposes tests for equality of the mean regression (MR) and quantile regression (QR) coefficients. The tests are based on the asymptotic joint distribution of the ordinary least squares and QR estimators. First, we formally derive the asymptotic joint distribution of these estimators. Second, we propose a Wald test for equality of the MR and QR coefficients considering a single fixed quantile, and also describe a more general test using multiple quantiles simultaneously. A very salient feature of these tests is that they produce asymptotically distribution-free nature of inference. In addition, we suggest a sup-type test for equality of the coefficients uniformly over a range of quantiles. For the estimation of the variance-covariance matrix, the use sample counterparts and bootstrap methods. An important attribute of the proposed tests is that they can be used as a heteroskedasticity test. Monte Carlo studies are conducted to evaluate the finite sample properties of the tests in terms of size and power. Finally, we briefly illustrate the implementation of the tests using Engel data.

We compare the two dominant approaches to estimation of benchmark damages in antitrust litigation, the forecasting approach and the dummy variable approach. We give conditions under which the two approaches are equivalent and present the results of a small simulation study.