Individual and time effects in nonlinear panel models with large N, T

We derive fixed effects estimators of parameters and average partial effects in (possibly dynamic) nonlinear panel data models with individual and time effects. They cover logit, probit, ordered probit, Poisson and Tobit models that are important for many empirical applications in micro and macroeconomics. Our estimators use analytical and jackknife bias corrections to deal with the incidental parameter problem, and are asymptotically unbiased under asymptotic sequences where N/T converges to a constant. We develop inference methods and show that they perform well in numerical examples.https://arxiv.org/abs/1311.7065Accepted manuscrip

The sorted effects method: discovering heterogeneous effects beyond their averages

Supplemental Data & Programs are available here: https://hdl.handle.net/2144/34409The partial (ceteris paribus) effects of interest in nonlinear and interactive linear models are heterogeneous as they can vary dramatically with the underlying observed or unobserved covariates. Despite the apparent importance of heterogeneity, a common practice in modern empirical work is to largely ignore it by reporting average partial effects (or, at best, average effects for some groups). While average effects provide very convenient scalar summaries of typical effects, by definition they fail to reflect the entire variety of the heterogeneous effects. In order to discover these effects much more fully, we propose to estimate and report sorted effects -- a collection of estimated partial effects sorted in increasing order and indexed by percentiles. By construction the sorted effect curves completely represent and help visualize the range of the heterogeneous effects in one plot. They are as convenient and easy to report in practice as the conventional average partial effects. They also serve as a basis for classification analysis, where we divide the observational units into most or least affected groups and summarize their characteristics. We provide a quantification of uncertainty (standard errors and confidence bands) for the estimated sorted effects and related classification analysis, and provide confidence sets for the most and least affected groups. The derived statistical results rely on establishing key, new mathematical results on Hadamard differentiability of a multivariate sorting operator and a related classification operator, which are of independent interest. We apply the sorted effects method and classification analysis to demonstrate several striking patterns in the gender wage gap.https://arxiv.org/abs/1512.05635Accepted manuscrip

Nonlinear Factor Models for Network and Panel Data

Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer of incidental parameter bias. We show how models with factor structures can also be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors.Comment: 49 pages, 6 tables, the changes in v4 include numerical results with more simulations and minor edits in the main text and appendi

Nonseparable Multinomial Choice Models in Cross-Section and Panel Data

Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identification of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimensional unobserved heterogeneity with cross-section and panel data. We show that derivatives of choice probabilities with respect to continuous attributes are weighted averages of utility derivatives in cross-section models with exogenous heterogeneity. In the special case of random coefficient models with an independent additive effect, we further characterize that the probability derivative at zero is proportional to the population mean of the coefficients. We extend the identification results to models with endogenous heterogeneity using either a control function or panel data. In time stationary panel models with two periods, we find that differences over time of derivatives of choice probabilities identify utility derivatives "on the diagonal," i.e. when the observed attributes take the same values in the two periods. We also show that time stationarity does not identify structural derivatives "off the diagonal" both in continuous and multinomial choice panel models.Comment: 23 page

Nonseparable sample selection models with censored selection rules: an application to wage decompositions

We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from integration over ranges of values of the control function. We provide conditions under which these objects are appropriate for the total population. We also present results regarding the estimation of counterfactual distributions. We derive conditions for identification for these different objects and suggest strategies for estimation. We also provide the associated asymptotic theory. These strategies are illustrated in an empirical investigation of the determinants of female wages and wage growth in the United Kingdom.https://arxiv.org/abs/1801.08961First author draf

Aproximación a los derechos de la infancia en la era de la globalización

Publicad

El tratamiento en el cine de la evolución histórica de la aplicación de la pena de muerte: de la crucifixión a la inyección letal

I Congreso Internacional de Historia y Cine: 5, 6, 7 y 8 de Septiembre de 2007

Modelling the urban distribution of goods

Las grandes ciudades actuales sufren una solicitación de su viario urbano excesiva para sus capacidades, de lo que resultan problemas de congestión y afectaciones al entorno que deben resolverse para el buen devenir de la ciudad y la mejora de la calidad de vida de sus ciudadanos. La mencionada solicitación se debe en parte a la distribución urbana de mercancías, en adelante DUM, por lo que se presume necesario su análisis y estudio, para poder aplicar mejoras en su gestión, y de esta manera contribuir a la reducción de dicha solicitación del viario urbano y/o a la optimización de la coexistencia de la DUM con otros modos de transporte. Con el propósito de analizar la DUM, se indica el tipo de actividad, de vehículo, de mercancías, de movimientos y delimitación horaria que se van a tener en cuenta para su clasificación. Posteriormente se deben clasificar funcionalmente los tipos de DUM, es decir, exponer que tipos existen en función de diferentes factores. De esta manera se podrá realizar un análisis certero para cada ruta y establecimiento en concreto, que permitirá identificar sus características así como sus puntos a favor y en contra. La clasificación funcional se según los siguientes aspectos

Generic inference on quantile and quantile effect functions for discrete outcomes

Quantile and quantile effect functions are important tools for descriptive and inferential analysis due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This paper offers a simple, practical construction of simultaneous confidence bands for quantile and quantile effect functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for distribution functions, which are readily available for many problems. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling strategies and does not depend on the sampling scheme. We apply our method to characterize the distributional impact of insurance coverage on health care utilization and obtain the distributional decomposition of the racial test score gap. Our analysis generates new, interesting empirical findings, and complements previous analyses that focused on mean effects only. In both applications, the outcomes of interest are discrete rendering existing inference methods invalid for obtaining uniform confidence bands for quantile and quantile effects functions.https://arxiv.org/abs/1608.05142First author draf