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Quantile selection models : with an application to understanding changes in wage inequality

Arellano, Manuel; Bonhomme, Stéphane - 2015

We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing...

Persistent link: https://www.econbiz.de/10011405705

Bayesian exploratory factor analysis

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; … - 2014

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...

Persistent link: https://www.econbiz.de/10010382148

Efficient estimation of copula-based semiparametric Markov models

Chen, Xiaohong; Wu, Wei Biao; Yi, Yanping - 2009

models are characterized by nonparametric invariant distributions and parametric copula functions; where the copulas capture … copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes … generated via Clayton, Gumbel and Student’s t copulas (with tail dependence) are all geometric ergodic. We then propose a sieve …

Persistent link: https://www.econbiz.de/10003817253

Layered policy analysis program evaluation using the marginal treatment effect

Mourifié, Ismael; Wan, Yuanyuan - 2021 - This version: Sunday 4th April, 2021

This paper proposes a unified approach to derive sharp bounds on all conventional policy parameters when the instrumental variables (IVs) are potentially invalid. Using a Vine Copula approach, we propose a novel characterization of the identified sets for the marginal treatment effect (MTE) and...

Persistent link: https://www.econbiz.de/10012508661

Estimation of nonparametric conditional moment models with possibly nonsmooth moments

Chen, Xiaohong (contributor); Pouzo, Demian (contributor) - 2008

This paper studies nonparametric estimation of conditional moment models in which the residual functions could be nonsmooth with respect to the unknown functions of endogenous variables. It is a problem of nonparametric nonlinear instrumental variables (IV) estimation, and a difficult nonlinear...

Persistent link: https://www.econbiz.de/10003739667

Nonparametric estimation of a polarization measure

Anderson, Gordon; Linton, Oliver; Whang, Yoon-jae - 2009

This paper develops methodology for nonparametric estimation of a polarization measure due to Anderson (2004) and Anderson, Ge, and Leo (2006) based on kernel estimation techniques. We give the asymptotic distribution theory of our estimator, which in some cases is nonstandard due to a boundary...

Persistent link: https://www.econbiz.de/10003847572

Uniform confidence bands for functions estimated nonparametrically with instrumental variables

Horowitz, Joel; Lee, Sokbae - 2009

This paper is concerned with developing uniform confidence bands for functions estimated nonparametrically with instrumental variables. We show that a sieve nonparametric instrumental variables estimator is pointwise asymptotically normally distributed. The asymptotic normality result holds in...

Persistent link: https://www.econbiz.de/10003869256

Intersection bounds : estimation and inference

Chernozhukov, Victor; Lee, Sokbae; Rosen, Adam M. - 2009

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set. Our approach is...

Persistent link: https://www.econbiz.de/10003869258

Statistical inference on regression with spatial dependence

Robinson, Peter M.; Thawornkaiwong, Supachoke - 2011

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

Persistent link: https://www.econbiz.de/10008859690

Asymptotic theory for nonparametric regression with spatial data

Robinson, Peter M. - 2011

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...

Persistent link: https://www.econbiz.de/10008906533

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