Constrained smoothing splines

We use smoothing splines to introduce prior information in nonparametric models. The type of information we consider is based on the belief that the regression curve is similar in shape to a parametric model. The resulting estimator is a convex sum of a fit to data and the parametric model, and it can be seen as shrinkage of the smoothing spline toward the parametric model. We analyze its rates of convergence and we provide some asymptotic distribution theory. Because the asymptotic distribution is intractable, we propose to carry out inference with the estimator by using the method proposed by Politis and Romano (1994, AnnalsofStatistics 22, 2031–2050). We also propose a data-driven technique to compute the smoothing parameters that provides asymptotically optimal estimates. Finally, we apply our results to the estimation of a model of investment behavior of the U.S. telephone industry and we present some Monte Carlo results

Semiparametric estimation of separable models with possibly limited dependent variables

In this paper we introduce a general method for estimating semiparametrically the different components in separable models+ The family of separable models is quite popular in economic research because this structure offers clear interpretation, has straightforward economic consequences, and is often justified by theory+ This family is also of statistical interest because it allows us to estimate high-dimensional complexity semiparametrically without running into the curse of dimensionality+ We consider even the case when multiple indices appear in the objective function; thus we can estimate models that are typical in economic analysis, such as those that contain limited dependent variables+ The idea of the new method is mainly based on a generalized profile likelihood approach+ Although this requires some hypotheses on the conditional error distribution, it yields a quite general usable method with low computational costs but high accuracy even for small samples+ We give estimation procedures and provide some asymptotic theory+ Implementation is discussed; simulations and an application demonstrate its feasibility and good finite-sample behavior

Empirical likelihood based inference for fixed effects varying coefficient

ABSTRACT: In this paper local empirical likelihood-based inference for non-parametric varying coefficient panel data models with fixed effects is investigated. First, we show that the naive empirical likelihood ratio is asymptotically standard chi-squared when undersmoothing is employed. The ratio is self-scale invariant and the plug-in estimate of the limiting variance is not needed. Second, mean-corrected and residual-adjusted empirical likelihood ratios are proposed. The main interest of these techniques is that without undersmoothing, both also have standard chi-squared limit distributions. As a by product, we propose also two empirical maximum likelihood estimators of the varying coefficient models and their derivatives. We also obtain the asymptotic distribution of these estimators. Furthermore, a non parametric version of the Wilk?s theorem is derived. To show the feasibility of the technique and to analyse its small sample properties, using empirical likelihood-based inference we implement a Monte Carlo simulation exercise and we also illustrated the proposed technique in an empirical analysis about the production efficiency of the European Union?s companies

Empirical likelihood based inference for a categorical varying-coefficient panel data model with fixed effects

ABSTRACT: In this paper local empirical likelihood-based inference for nonparametric categorical varying coefficient panel data models with fixed effects under cross-sectional dependence is investigated. First, we show that the naive empirical likelihood ratio is asymptotically standard chi-squared using a nonparametric version of Wilks? theorem. The ratio is self-scale invariant and the plug-in estimate of the limiting variance is not needed. As a by product, we propose also an empirical maximum likelihood estimator of the categorical varying coefficient model and we obtain the asymptotic distribution of this estimator. We also illustrated the proposed technique in an application that reports estimates of strike activities from 17 OECD countries for the period 1951-85

Nonparametric estimation of time varying parameters under shape restrictions

In this paper we propose a new method to estimate nonparametrically a time varying parameter model when some qualitative information from outside data (e.g. seasonality) is available. In this framework we make two main contributions. First, the resulting estimator is shown to belong to the class of generalized ridge estimators and under some conditions its rate of convergence is optimal within its smoothness class. Furthermore, if the outside data information is fullfilled by the underlying model, the estimator shows efficiency gains in small sample sizes. Second, for the implementation process, since the estimation procedure envolves the computation of the inverse of a high order matrix we provide an algorithm that avoids this computation and, also, a data-driven method is derived to select the control parameters. The practical performance of the method is demonstrated in a simulation study and in an application to the demand of soft drinks in Canada

A new approach to understanding labour supply of disabled people : The efects of job-type characteristics on participation decisions.

ABSTRACT: The main interest of this article is to propose an individual utility maximization model to explain the low participation of disabled people. We account for heterogeneity of preferences and furthermore time of self-caring for disabled individuals is considered as an argument in the utility function. The hours of work decided by disabled individuals are neither homogeneous (they depend on unknown characteristics) nor continuous (discrete choice sets). We use data of 4790 households from the Spanish Survey of Disability, Personal Autonomy and Dependency and find association between time of informal care and labour participation and, consequently, the choice between jobs

Two-stage nonparametric regression for longitudinal data

In the analysis of longitudinal data it is of main interest to investigate the existence of group and individual effects under correlated observations across time. In this paper, we develop a nonparametric two-step procedure that enables us to estimate group effects under a very general form of correlation across time. Moreover, we propose several methods to estimate the bandwidth and show their asymptotyc optimality. Since the asymptotic distribution is untractable, we develop a randomization test that is suitable for testing the group effects. Finally, we apply the estimation procedure, the bandwidth selection criteria and the randomization test to the data from the Iowa Cochlear Implant Project

Finite sample behavior of two step estimators in selection models

The problem of specification errors in sample selection models has received considerable attention both theoretically and empirically. However, very few is known about the finite sample behavior of two step estimators. In this paper we investigate by simulations both bias and finite sample distribution of these estimators when ignoring heteroskedasticity in the sample selection mechanism. It turns out that under conditions traditionally faced by practitioners, the misspecified parametric two step estimator (Heckman, 1979) performs better, in finite sample sizes, than the robust semiparametric one (Ahn and Powell, 1993). Moreover, under very general conditions, we show that the asymptotic bias of the parametric two step estimator is linear in the covariance between the sample selection and the participation equation

Estimación de los efectos redistributivos y de las ganancias en bienestar social derivados de la progresividad del IRPF en las Comunidades Autonómas del territorio de Régimen Común

RESUMEN. En este artículo, en primer lugar, exponemos las principales características de los registros administrativos de la información fiscal sobre la tributación efectiva del IRPF agrupada por tramos de base imponible gravada media, procedente de la Dirección General de Informática Tributaria de la Agencia Tributaria. En segundo lugar, siguiendo la metodología expuesta por Kakwani y Podder (1976), estimamos, para cada Comunidad Autónoma del Territorio de Régimen Común, curvas de Lorenz y de concentración y los correspondientes índices de desigualdad asociados. A continuación, y siguiendo a Creedy (1996), estimamos para cada Comunidad Autónoma las ganancias en bienestar social asociadas a la progresividad del IRPF. Por último, presentamos los valores estimados de progresividad del IRPF, capacidad redistributiva y ganancias en bienestar social, para cada Comunidad Autónoma, analizando brevemente sus características y evolución.ABSTRACT. This work first examines the main characteristics of the administrative tax records on effective income tax payment grouped by taxable income bands, provided by the Tax Authority’s General Directorate for Tax Information Technology. Secondly, following the methodology set out by Kakwani and Podder (1976), we estimate Lorentz and concentration curves and the corresponding indices of associated inequality for each of Spain’s Autonomous Regions. Subsequently, and based on Creedy (1996), we estimate the gains in social welfare associated to income tax progressiveness for each Autonomous Region. Finally, we present the estimated values of income tax progressiveness, redistribution capacity and gains in social welfare for each Autonomous Region, briefly analysing their characteristics and evolution

Parametric and Semiparametric approaches in the estimation of a job-search model.

This paper analyzes empirically the labor supply in the spanish labor market using different econometric techniques to estimate the reduced form models. Mainly we use maximum likelihood and two step estimation methods under different assumptions as heteroskedasticity and non-normality. Among two step methods we consider the approach developed by Heckman and other two step estimation procedures based on semiparametric methods. We also perform several specification tests and we compare the results obtained under different specifications