3,233 research outputs found
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
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.
Bias corrections for two-step fixed effects panel data estimators
This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with unobserved individual effects.
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
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
We develop a distribution regression model under endogenous sample selection.
This model is a semiparametric generalization of the Heckman selection model
that accommodates much richer patterns of heterogeneity in the selection
process and effect of the covariates. The model applies to continuous, discrete
and mixed outcomes. We study the identification of the model, and develop a
computationally attractive two-step method to estimate the model parameters,
where the first step is a probit regression for the selection equation and the
second step consists of multiple distribution regressions with selection
corrections for the outcome equation. We construct estimators of functionals of
interest such as actual and counterfactual distributions of latent and observed
outcomes via plug-in rule. We derive functional central limit theorems for all
the estimators and show the validity of multiplier bootstrap to carry out
functional inference. We apply the methods to wage decompositions in the UK
using new data. Here we decompose the difference between the male and female
wage distributions into four effects: composition, wage structure, selection
structure and selection sorting. After controlling for endogenous employment
selection, we still find substantial gender wage gap -- ranging from 21% to 40%
throughout the (latent) offered wage distribution that is not explained by
observable labor market characteristics. We also uncover positive sorting for
single men and negative sorting for married women that accounts for a
substantive fraction of the gender wage gap at the top of the distribution.
These findings can be interpreted as evidence of assortative matching in the
marriage market and glass-ceiling in the labor market.Comment: 72 pages, 4 tables, 39 figures, includes supplement with additional
empirical result
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
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
- …