The dissertation consists of three essays. The main focus is set on semiparametric regression modeling, which embodies the strength of parametric and nonparametric regression models in terms of flexibility, dimensionality and interpretability. The first essay is “Some Recent Advances in...

While the traditional R value is useful to evaluate the quality of a fit, it does not work when it comes to evaluating the predictive power of estimated financial models in finite samples.

We propose a complete framework for model-free difference-in-differences analysis with covariates, where model-free means data-driven, in particular nonparametric estimation and testing, variable and scale choice. We start with searching for the preferred data setup by simultaneously choosing...

Economists use partial and general equilibrium trade simulation models toestimate the impact of changes in domestic policies and international trade rules.During the WTO Doha Development Agenda (DDA) negotiations economists haveproduced many different estimates of the gains that would result...

Models are studied where the response Y and covariates X, T are assumed to fulfill E(Y

We consider problems in modelling job-matching in the Czech Republic during the transition to a market economy. Special interest is devoted to functional form considerations and the analysis of returns to scale of the matching function. This explorative study aims to shed some light into the...

We consider an additive model with second order interaction terms. It is shown how the components of this model can be estimated using marginal integration, and the asymptotic distribution of the estimators is derived. Moreover, two test statistics for testing the presence of interactions are...

The aim of this paper is to provide an alternative way of specification and estimation of a labor supply model. The proposed estimation procedure can be included in the so called predicted wage methods and its main interest is twofold. First, under standard assumptions in studies of labor...