A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to...

Dummy endogenous variables are commonly encountered in program evaluations using observational data. Motivated by the increasing availability of rich micro data, we develop a two-stage approach to estimate the dummy endogenous treatment effect using high-dimensional instrumental variables (IV)....

This paper presents a method for estimating the average treatment effects (ATE) of an exponential endogenous switching model where the coefficients of covariates in the structural equation are random and correlated with the binary treatment variable. The estimating equations are derived under...

This article discusses the prospects of using linear regression models to describe multi-section branched transport systems of conveyor type. A characteristic feature of the functioning of a multi-section transport system is the presence of resonant peak values for the flow parameters of the...

This paper focuses the development of the diagnostics for the perturbations of case-weights and explanatory variables (one or more) in a linear logistic regression model. The effect of specific perturbation scheme on the estimation of parameters is also assessed. In addition, the interpretation...

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed "initial conditions problem," as well as the...

The paper compares two approaches to the estimation of panel probit models: the Generalized Method of Moments (GMM) and the Simulated Maximum Likelihood (SML) technique. Both have in common that they circumvent multiple integrations of joint density functions without the need to impose...

We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation...

This paper studies inference on fixed effects in a linear regression model estimated from network data. We derive bounds on the variance of the fixed-effect estimator that uncover the importance of the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and of the degree...