This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman?s (1976, 1979) two?step estimator for estimating a selection model. It shows that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full?information maximum likelihood estimator is preferable to the limited?information two?step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two?Part Model) is the most robust amongst the simple?to? calculate estimators. --
In this paper we discuss the differences between the average marginal effect and the marginal effect...
The chapters of this dissertation are devoted to three different topics. The first chapter studies e...
Adaptive generation of hypotheses is among the main culprits of the lack of replicability in science...
This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979)...
This analysis shows that multivariate generalizations to the classical Heckman (1976 and 1979) two-s...
In this paper we discuss the differences between the average marginal effect and the marginal effect...
The classical Heckman (1976, 1979) selection correction estimator (heckit) is misspecified and inco...
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to ...
Sample selection arises when the outcome of interest is partially observed in a study. A common cha...
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to ...
This paper describes the implementation of Heckman-type sample selection models in R. We discuss the...
The paper provides Monte Carlo evidence on the performance of general-to-specific and specific-to-ge...
This study applies a full information maximum likelihood (FIML) estimator of the sample selection mo...
In this presentation, we illustrate an application of a relatively new selection-bias correction met...
This article constructs and evaluates Lagrange multiplier (LM) and Neyman's C(α) tests based on biva...
In this paper we discuss the differences between the average marginal effect and the marginal effect...
The chapters of this dissertation are devoted to three different topics. The first chapter studies e...
Adaptive generation of hypotheses is among the main culprits of the lack of replicability in science...
This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979)...
This analysis shows that multivariate generalizations to the classical Heckman (1976 and 1979) two-s...
In this paper we discuss the differences between the average marginal effect and the marginal effect...
The classical Heckman (1976, 1979) selection correction estimator (heckit) is misspecified and inco...
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to ...
Sample selection arises when the outcome of interest is partially observed in a study. A common cha...
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to ...
This paper describes the implementation of Heckman-type sample selection models in R. We discuss the...
The paper provides Monte Carlo evidence on the performance of general-to-specific and specific-to-ge...
This study applies a full information maximum likelihood (FIML) estimator of the sample selection mo...
In this presentation, we illustrate an application of a relatively new selection-bias correction met...
This article constructs and evaluates Lagrange multiplier (LM) and Neyman's C(α) tests based on biva...
In this paper we discuss the differences between the average marginal effect and the marginal effect...
The chapters of this dissertation are devoted to three different topics. The first chapter studies e...
Adaptive generation of hypotheses is among the main culprits of the lack of replicability in science...