Specification testing and quasi-maximum-likelihood estimation
TLDR
This article developed robust regression-based conditional moment tests for models estimated by quasi-maximum-likelihood using a density in the linear exponential family, which are relatively simple to compute, while being robust to distributional assumptions other than those being explicitly tested.About:
This article is published in Journal of Econometrics.The article was published on 1991-04-01 and is currently open access. It has received 87 citations till now. The article focuses on the topics: Conditional variance & Conditional probability distribution.read more
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Book
Econometric Analysis of Cross Section and Panel Data
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Posted Content
Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates
TL;DR: In this paper, simple quasi-likelihood methods for estimating regression models with a fractional dependent variable and for performing asymptotically valid inference are proposed, and they apply these methods to a data set of employee participation rates in 401(k) pension plans.
Journal ArticleDOI
Econometric methods for fractional response variables with an application to 401 (k) plan participation rates
TL;DR: In this paper, the authors develop attractive functional forms and simple quasi-likelihood estimation methods for regression models with a fractional dependent variable, and apply these methods to a data set of employee participation rates in 401 (k) pension plans.
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Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov,Denis Chetverikov,Mert Demirer,Esther Duflo,Christian Hansen,Whitney K. Newey,James M. Robins +6 more
TL;DR: In this article, the authors show that the impact of regularization bias and overfitting on estimation of the parameter of interest θ0 can be removed by using two simple, yet critical, ingredients: (1) using Neyman-orthogonal moments/scores that have reduced sensitivity with respect to nuisance parameters, and (2) making use of cross-fitting, which provides an efficient form of data-splitting.
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Inverse probability weighted estimation for general missing data problems
TL;DR: The authors study inverse probability weighted M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect in the linear exponential family, and variable probability sampling with observed retention frequencies.
References
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A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
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Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
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Specification Tests in Econometrics
TL;DR: In this article, the null hypothesis of no misspecification was used to show that an asymptotically efficient estimator must have zero covariance with its difference from a consistent but asymptonically inefficient estimator, and specification tests for a number of model specifications in econometrics.
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Large sample properties of generalized method of moments estimators
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Maximum likelihood estimation of misspecified models
TL;DR: In this article, the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference are examined, and the properties of the quasi-maximum likelihood estimator and the information matrix are exploited to yield several useful tests.