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Author: Sidharth Kankanala
Publisher:
ISBN:
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Languages : en
Pages :
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Book Description
"Instrumental variables are widely used in applied statistics and econometrics to achieve identification and carry out inference in models that contain endogenous explanatory variables. In the usual setup the function of interest is assumed to be known up to finitely many unknown parameters and instrumental variables aid in identification of these parameters. However, this is a strong assumption that is rarely justified by economic theory and so nonparametric methods provide a more flexible alternative to model endogenous data in the sense no assumptions on the parametric form of a function are required. In this thesis we first examine the role of a single instrumental variable to achieve identification in a linear model through the stronger conditional moment restriction assumption that is usually imposed in the nonparametric framework. We do this by approximating the conditional moment restriction by an increasing sequence of moment restrictions that correspond to discretizing/binning the instrumental variable. Finally, we examine the nonparametric instrumental variable model when the explanatory variable has been discretized to provide a growing approximation of the unknown function and the instrumental variable has been discretized to approximate the conditional moment restriction." --
Author: Sidharth Kankanala
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"Instrumental variables are widely used in applied statistics and econometrics to achieve identification and carry out inference in models that contain endogenous explanatory variables. In the usual setup the function of interest is assumed to be known up to finitely many unknown parameters and instrumental variables aid in identification of these parameters. However, this is a strong assumption that is rarely justified by economic theory and so nonparametric methods provide a more flexible alternative to model endogenous data in the sense no assumptions on the parametric form of a function are required. In this thesis we first examine the role of a single instrumental variable to achieve identification in a linear model through the stronger conditional moment restriction assumption that is usually imposed in the nonparametric framework. We do this by approximating the conditional moment restriction by an increasing sequence of moment restrictions that correspond to discretizing/binning the instrumental variable. Finally, we examine the nonparametric instrumental variable model when the explanatory variable has been discretized to provide a growing approximation of the unknown function and the instrumental variable has been discretized to approximate the conditional moment restriction." --
Author: Neil R. Ericsson
Publisher:
ISBN: 9780198774044
Category : Business & Economics
Languages : en
Pages : 436
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Book Description
This book discusses the nature of exogeneity, a central concept in standard econometrics texts, and shows how to test for it through numerous substantive empirical examples from around the world, including the UK, Argentina, Denmark, Finland, and Norway. Part I defines terms and provides the necessary background; Part II contains applications to models of expenditure, money demand, inflation, wages and prices, and exchange rates; and Part III extends various tests of constancy and forecast accuracy, which are central to testing super exogeneity. About the Series Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
Author: Samuele Centorrino
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Category :
Languages : en
Pages :
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Author: Joachim Freyberger
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
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This paper provides a first test for the identification condition in a nonparametric instrumental variable model, known as completeness, by linking the outcome of the test to consistency of an estimator. In particular, I show that uniformly over all distributions for which the test rejects with probability bounded away from 0, an estimator of the structural function is consistent. This is the case for a large class of complete distributions as well as certain sequences of incomplete distributions. As a byproduct of this result, the paper makes two additional contributions. First, I present a definition of weak instruments in the nonparametric instrumental variable model, which is equivalent to the failure of a restricted version of completeness. Second, I show that the null hypothesis of weak instruments, and thus failure of a restricted version of completeness, is testable and I provide a test statistic and a bootstrap procedure to obtain the critical values. Finally, I demonstrate the finite sample properties of the tests and the estimator in Monte Carlo simulations.
Author: Serge Darolles
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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The focus of the paper is the nonparametric estimation of an instrumental regression function f defined by conditional moment restrictions stemming from a structural econometric model: E [Y - f (Z) | W] = 0, and involving endogenous variables Y and Z and instruments W. The function f is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function.
Author: Naoya Sueishi
Publisher:
ISBN:
Category :
Languages : en
Pages : 145
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Author: Denis Chetverikov
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Category :
Languages : en
Pages :
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The ill-posedness of the inverse problem of recovering a regression function in a nonparametric instrumental variable (NPIV) model leads to estimators that may suffer from poor statistical performance. In this paper, we explore the possibility of imposing shape restrictions to improve the performance of the NPIV estimators. We assume that the regression function is monotone and consider sieve estimators that enforce the monotonicity constraint. We define a restricted measure of ill-posedness that is relevant for the constrained estimators and show that under the monotone IV assumption and certain other conditions, our measure of ill-posedness is bounded uniformly over the dimension of the sieve space, in stark contrast with a well-known result that the unrestricted sieve measure of ill-posedness that is relevant for the unconstrained estimators grows to infinity with the dimension of the sieve space. Based on this result, we derive a novel non-asymptotic error bound for the constrained estimators. The bound gives a set of data-generating processes where the monotonicity constraint has a particularly strong regularization effect and considerably improves the performance of the estimators. The bound shows that the regularization effect can be strong even in large samples and for steep regression functions if the NPIV model is severely ill-posed a finding that is confirmed by our simulation study. We apply the constrained estimator to the problem of estimating gasoline demand from U.S. data.
Author: Michael Lechner
Publisher: Springer Science & Business Media
ISBN: 364257615X
Category : Business & Economics
Languages : en
Pages : 248
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Book Description
Empirical measurement of impacts of active labour market programmes has started to become a central task of economic researchers. New improved econometric methods have been developed that will probably influence future empirical work in various other fields of economics as well. This volume contains a selection of original papers from leading experts, among them James J. Heckman, Noble Prize Winner 2000 in economics, addressing these econometric issues at the theoretical and empirical level. The theoretical part contains papers on tight bounds of average treatment effects, instrumental variables estimators, impact measurement with multiple programme options and statistical profiling. The empirical part provides the reader with econometric evaluations of active labour market programmes in Canada, Germany, France, Italy, Slovak Republic and Sweden.
Author: William A. Barnett
Publisher: Cambridge University Press
ISBN: 9780521424318
Category : Business & Economics
Languages : en
Pages : 512
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Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Author: James Joseph Heckman
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 40
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This paper exposits and relates two distinct approaches to bounding the average treatment effect. One approach, based on instrumental variables, is due to Manski (1990, 1994), who derives tight bounds on the average treatment effect under a mean independence form of the instrumental variables (IV) condition. The second approach, based on latent index models, is due to Heckman and Vytlacil (1999, 2000a), who derive bounds on the average treatment effect that exploit the assumption of a nonparametric selection model with an exclusion restriction. Their conditions imply the instrumental variable condition studied by Manski, so that their conditions are stronger than the Manski conditions. In this paper, we study the relationship between the two sets of bounds implied by these alternative conditions. We show that: (1) the Heckman and Vytlacil bounds are tight given their assumption of a nonparametric selection model; (2) the Manski bounds simplify to the Heckman and Vytlacil bounds under the nonparametric selection model assumption.
