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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

Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals

Chen, Xiaohong; Pouzo, Demian - 2009

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals … procedure consistently estimates the limiting distribution of the PSMD ˆΘ; (3) the semiparametric efficiency bound formula of Ai … generalized residuals ; Nonlinear nonparametric endogeneity ; Weighted bootstrap ; Semiparametric efficiency ; Confidence region …

Persistent link: https://www.econbiz.de/10003869261

Uniform confidence bands for functions estimated nonparametrically with instrumental variables

Horowitz, Joel; Lee, Sokbae - 2010

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 mental variables estimator is pointwise asymptotically...

Persistent link: https://www.econbiz.de/10003990115

Conditional quantile processes based on series or many regressors

Belloni, Alexandre; Chernozhukov, Victor; … - 2011

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...

Persistent link: https://www.econbiz.de/10009153247

Identification and shape restrictions in nonparametric instrumental variables estimation

Freyberger, Joachim; Horowitz, Joel - 2012

This paper is concerned with inference about an unidentified linear functional, L(g), where the function g satisfies the relation Y=g(x) + U; E(U/W) = 0. In this relation, Y is the dependent variable, X is a possibly endogenous explanatory variable, W is an instrument for X, and U is an...

Persistent link: https://www.econbiz.de/10009554348

Identification and shape restrictions in nonparametric instrumental variables estimation

Freyberger, Joachim; Horowitz, Joel - 2013

This paper is concerned with inference about an unidentified linear function, L(g), where the function g satisfies the relation Y=g(X)+U; E(U |W)=0. In this relation, Y is the dependent variable, X is a possibly endogenous explanatory variable, W is an instrument for X and U is an unobserved...

Persistent link: https://www.econbiz.de/10009761386

Testing for a general class of functional inequalities

Lee, Sokbae; Song, Kyungchul; Whang, Yoon-jae - 2014

In this paper, we propose a general method for testing inequality restrictions on nonparametric functions. Our framework includes many nonparametric testing problems in a uni ed framework, with a number of possible applications in auction models, game theoretic models, wage inequality, and...

Persistent link: https://www.econbiz.de/10010254852

Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression

Chen, Xiaohong; Christensen, Timothy M. - 2017 - Revised: January 25, 2017

This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and its functionals. First, we derive sup-norm convergence rates for computationally simple sieve NPIV (series 2SLS)...

Persistent link: https://www.econbiz.de/10011596624

Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models

Parente, Paulo M. D. C.; Smith, Richard J. - 2019 - This Draft: October 2019

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi-log likelihood function in an appropriate way and then samples...

Persistent link: https://www.econbiz.de/10012115888

Implied probability kernel block bootstrap for times series moment condition models

Parente, Paulo M. D. C.; Smith, Richard J. - 2024

This article generalizes and extends the kernel block bootstrap (KBB) method of Parente and Smith (2018, 2021) to provide a comprehensive treatment of its use for GMM estimation and inference in time-series models formulated in terms of moment conditions. KBB procedures that employ bootstrap...

Persistent link: https://www.econbiz.de/10014520806