Maximum likelihood, Optimization, C87,

Many statistical analyses (e.g., in econometrics, biostatistics and experimental design) are based on models containing systems of structurally related equations. The systemfit package provides the capability to estimate systems of linear equations within the R programming environment. For instance, this package can be used for "ordinary least squares" (OLS), "seemingly unrelated regression" (SUR), and the instrumental variable (IV) methods "two-stage least squares" (2SLS) and "three-stage least squares" (3SLS), where SUR and 3SLS estimations can optionally be iterated. Furthermore, the systemfit package provides tools for several statistical tests. It has been tested on a variety of datasets and its reliability is demonstrated.

We show that the monotonicity condition is conceptually important in Stochastic Frontier Analysis (SFA). Despite its importance, most empirical studies do not impose monotonicity-probably because existing approaches are rather complex and laborious. Therefore, we propose a three-step procedure that is much simpler than existing approaches. We demonstrate how monotonicity of a translog function can be imposed regionally at a connected set (region) of input quantities. Our method can be applied not only to impose monotonicity on translog production frontiers but also to impose other restrictions on cost, distance, or profit frontiers.

This paper describes the implementation of Heckman-type sample selection models in R. We discuss the sample selection problem as well as the Heckman solution to it, and argue that although modern econometrics has non-and semiparametric estimation methods in its toolbox, Heckman models are an integral part of the modern applied analysis and econometrics syllabus. We describe the implementation of these models in the package sampleSelection and illustrate the usage of the package on several simulation and real data examples. Our examples demonstrate the effect of exclusion restrictions, identification at infinity and misspecification. We argue that the package can be used both in applied research and teaching.

The inverse hyperbolic sine (IHS) transformation is frequently applied in econometric studies to transform right-skewed variables that include zero or negative values. We show that regression results can largely depend on the units of measurement of IHS-transformed variables. Hence, arbitrary choices regarding the units of measurement for these variables can have a considerable effect on recommendations for policies or business decisions. In order to address this problem, we suggest a procedure for choosing units of measurement for IHS-transformed variables. A Monte Carlo simulation assesses this procedure under various scenarios and an empirical illustration shows the relevance and applicability of our suggested procedure.