Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. This sensitivity is addressed by the theory of robust statistics which builds upon parametric specification, but provides...

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. This sensitivity addressed by the theory of robust statistics which builds upon parametric specification, but provides methodology...

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. On the other hand, semiparametric and nonparametric methods, which are not restricted by parametric assumptions, require more data...

The intercept in endogenous selection models is of fundamental importance for the evaluationof average treatment effects. While various intercept estimators for additive linear selectionmodels exist, there are currently no estimators for nonlinear selection models. This paper introduces...

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identifed. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal...

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions...

This paper describes an empirical investigation into the predictive ability of four credit scoring models as applied to US personal loans. The models tested include the Logit model (LM), the divergence – a discriminant – method (DVM), neural networks (NN), and the generalized additive model...

In this paper, I study the estimation of nonlinear models of spatial processes. Generalized estimating equations (GEE) are applied to cross section data with spatial correlations. I use a partial quasi-maximum likelihood estimator (PQMLE) in the first step and use a GEE approach in the second...

This paper deals with heterogeneity and nonlinearities in the growth process by developing a two-stage strategy to identify and estimate a club convergence model with threshold externalities. Because of identification and collinearity problems, we develop an entropy-based estimation procedure...

We study a nonlinear two-way fixed effects panel model that allows for unobserved individual heterogeneity in slopes (interacting with covariates) and (unknown) flexibly specified link function. The former is particularly relevant when the researcher is interested in the distributional causal...