Using individual level data from Turkstat Household Labor Force Survey for 2005-2009 period and a variety of parametric and semi-parametric techniques, we test two hypothesis regarding formal and informal labor markets: whether there is a wage gap between formal and informal workers and whether...

Efficiency of education and health provision in a sample of OECD countries is assessed by means of two alternative non-parametric methodologies: FDH and DEA. The efficiency frontier is estimated using both measures of expenditure and quantity inputs, as a country may well be efficient from a...

The paper estimates the teaching efficiencies of Indian Higher Educational Institutions (HEIs) using Data Envelopment Analysis (DEA) according to their location, ownership, courses they specialise in and age, using recent data published by National Institutional Ranking Framework (NIRF). We find...

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semiparametric general trimmed estimator (GTE) of truncated...

This paper is concerned with developing a semiparametric panel model to explain the trend in UK temperatures and other weather outcomes over the last century. We work with the monthly averaged maximum and minimum temperatures observed at the twenty six Meteorological Office stations. The data is...

We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null and alternative hypotheses regarding conditional treatment effects, including (i) the null hypothesis of the conditional stochastic dominance between treatment...

This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error...

In this paper we develop procedures for performing inference in regression models about how potential policy interventions affect the entire marginal distribution of an outcome of interest. These policy interventions consist of either changes in the distribution of covariates related to the...

I introduce a procedure to nonparametrically estimate local quantile treatment effects in a regression discontinuity (RD) design with a binary treatment. Analogously to Hahn, Todd, and van der Klaauw's (2001) estimator for average treatment effects using local linear regression, the estimator...

The weighted Average Quantile Derivative (AQD) is the expected value of the partial derivative of the conditional quantile function (CQF) weighted by a function of the covariates. We consider two weighting functions: a known function chosen by researchers and the density function of the...