We develop semiparametric and instrumental-variables approaches to self-selection problems in comparisons of wage inequality between educational groups. We propose new estimators to identify the causal effect of college education on scale parameters of wage distributions, using symmetry conditions on the joint distribution of outcome and selection errors, along with kernel weighting schemes. A ...

This paper investigates cost and profit efficiency of German hospitals. More specifically, it deals with the question how hospital efficiency varies with ownership, patient structure, and other exogenous factors, which are neither inputs nor outputs of the production process. We conduct a Stochastic Frontier Analysis (SFA) on a multifaceted administrative German dataset combined with the balanc...

This paper investigates cost and profit efficiency of German hospitals. More specifically, it deals with the question how hospital efficiency varies with ownership, patient structure, and other exogenous factors, which are neither inputs nor outputs of the production process. We conduct a Stochastic Frontier Analysis (SFA) on a multifaceted administrative German dataset combined with the balanc...

This paper develops an asymptotic theory for a non-linear parametric co-integrating regression model. We establish a general framework for weak consistency that is easy to apply for various non-stationary time series, including partial sum of linear process and Harris recurrent Markov chain. We provide a limit distribution for the nonlinear least square estimator which significantly extends the...

This paper presents Genetic Matching, a method of multivariate matching, that uses an evolutionary search algorithm to determine the weight each covariate is given. Both propensity score matching and matching based on Mahalanobis distance are limiting cases of this method. The algorithm makes transparent certain issues that all matching methods must confront. We present simulation studies that ...

In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application docume...

We consider a stochastic environment to study interactions among pollution growth, demographic changes, and economic growth. Drawing on the empirical findings of slow convergence patterns of pollution shocks (viz., with a long-memory), we build an analytical framework where stochastic environmental feedback effects on population changes are reflected upon aggregate economic growth. Long-memory ...

When the overidentifying restrictions of the constant-effect linear instrumental variables model fail, common IV estimators converge to different probability limits. I characterize the estimands of two stage least squares, two step GMM, and limited information maximum likelihood as functions of the single-instrument estimands from the just-identified IV regressions which consider each instrumen...

This paper is concerned with developing uniform confidence bands for functions estimated nonparametrically with instrumental variables. We show that a sieve nonparametric instrumental variables estimator is pointwise asymptotically normally distributed. The asymptotic normality result holds in both mildly and severely ill-posed cases. We present an interpolation method to obtain a uniform confi...

This paper presents a Monte Carlo comparison of several versions of heteroscedasticity robust standard errors (HRSEs) to a nonparametric feasible generalized least squares procedure (NPGLS). Results suggest that the NPGLS procedure provides an improvement in efficiency ranging from 3% to 12% or more in reasonable sample sizes using simple functional forms for heteroscedasticity. This results in...