<p>This paper presents a life-cycle model of woman's labour supply, human capital formation and savings for the evaluation of welfare-to-work and tax policies. Women's decisions are formalised in a dynamic and uncertain environment. The model includes a detailed characterisation of the tax system...</p>

Central limit theorems are developed for instrumental variables estimates of linear and semiparametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss...

Panel data, whose series length T is large but whose cross-section size N need not be, are assumed to have a common time trend. The time trend is of unknown form, the model includes additive, unknown, individual-specific components, and we allow for spatial or other cross-sectional dependence...

Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well...

<p>This paper develops tests for inequality constraints of nonparametric regression functions. The test statistics involve a one-sided version of L_p-type functionals of kernel estimators. Drawing on the approach of Poissonization, this paper establishes that the tests are asymptotically...</p>

<p>We analyze the identification and estimation of parameters β satisfying the incomplete linear moment restrictions E(z ^T(x β−y)) = E(z^Tu(z)) where z is a set of instruments and u(z) an unknown bounded scalar function. We first provide empirically relevant examples of such a set-up. Second, we...</p>

This paper develops inference and statistical decision for set-identified parameters from the robust Bayes perspective. When a model is set-identified, prior knowledge for model parameters is decomposed into two parts: the one that can be updated by data (revisable prior knowledge) and the one...

This paper is concerned with testing rationality restrictions using quantile regression methods. Specifically, we consider negative semidefiniteness of the Slutsky matrix, arguably the core restriction implied by utility maximization. We consider a heterogeneous population characterized by a...

This paper contributes to the understanding of the source of identification in panel data models. Recent research has established that few time periods suffice to identify interesting structural effects in nonseparable panel data models even in the presence of complex correlated unobservables,...

<p><p>Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR series framework, covering many regressors as a special...</p></p>