"We show that the well-known numerical equivalence between two-stage least squares (2SLS) and the classic control function (CF) estimator raises an interesting and unrecognized puzzle. The classic CF approach maintains that the regression error is mean independent of the instruments conditional...

Rating variables indicate the extent to which a quality is present, or absent, in a unit of observation. In this paper, we discuss a class of non-linear regression models for rating dependent variables and their estimation by parametric and semi-parametric methods. An application to life...

This paper compares a nonparametric generalized least squares (NPGLS) estimator to parametric feasible GLS (FGLS) and variants of heteroscedasticity robust standard error estimators (HRSEs) in an applied setting. Given myriad alternative HRSEs, a clear consensus on which version to use does not...

This article proposes a new class of rating scale models, which merges advantages and overcomes shortcomings of the traditional linear and ordered latent regression models. Both parametric and semi-parametric estimation is considered. The insights of an empirical application to satisfaction data...

Data Envelopment Analysis (DEA), a nonparametric mathematical programming approach to productive efficiency analysis, envelops all observed data. In this paper we show that DEA can be interpreted as nonparametric least squares regression subject to shape constraints on frontier and sign...

Let (X,Y) be a pair of random variables with supp(X) \subseteq [0,1]?I and EY?2 \infinity. Let m* be the best approximation of the regression function of (X,Y) by sums of functions of at most d variables (formula). Estimation of m* from i.i.d. data is considered. For the estimation interaction...