Nonparametric regression is often considered as unreliable in small samples when the number of regressors is large. This may be different if the dependent variable is bounded. First, the small-sample properties of nonparametric regression with 14 regressors (4 continuous, 10 binary) are...

We consider the regression discontinuity (RD) design with categorical outcomes, and exploit the possibility of adapting well-developed microeconometric models to the RD setting. The channels through which the forcing variable affects the potential outcome distributions are constrained to be...

The Random Utility Model (RUM) is a workhorse model for valuing new products or changes in public goods. But RUMs have been faulted along two lines. First, for including idiosyncratic errors that imply unreasonably high values for new alternatives and unrealistic substitution patterns. Second,...

This paper deals with the identification of treatment effects when the outcome variable is ordered. If outcomes are measured ordinally, previously developed methods to investigate the impact of an endogenous binary regressor on average outcomes cannot be applied as the expectation of an ordered...

We develop novel regression discontinuity inferences where the binary treatment and/or continuous assignment variable may contain measurement errors. For a measurement error of the binary treatment, the standard estimator is inconsistent for the causal parameter. To solve the problem, we develop...

For estimating regression function we can use many proceedings. In this paper, we have chosen to apply scaling functions to the estimation of regression functions. When one knows many bivariate date with the values of two variables, in the goal to express a correlation between the two variables...

We consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε. Both estimators are analysed and compared in the sense of mean-squared error. We consider the case of...

Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems.To...