On Completeness and Consistency in Nonparametric Instrumental Variable Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download On Completeness and Consistency in Nonparametric Instrumental Variable Models PDF full book. Access full book title On Completeness and Consistency in Nonparametric Instrumental Variable Models by Joachim Freyberger. Download full books in PDF and EPUB format.
Author: Joachim Freyberger
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Get Book Here
Book Description
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: Joachim Freyberger
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
Get Book Here
Book Description
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
Get Book Here
Book Description
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: Jeffrey S. Racine
Publisher: Cambridge University Press
ISBN: 1108483402
Category : Business & Economics
Languages : en
Pages : 435
Get Book Here
Book Description
Provides theory, open source R implementations, and the latest tools for reproducible nonparametric econometric research.
Author: Bruce Hansen
Publisher: Princeton University Press
ISBN: 0691236151
Category : Business & Economics
Languages : en
Pages : 1081
Get Book Here
Book Description
The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds Draws on integrated, research-level datasets, provided on an accompanying website Discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning Features hundreds of exercises that enable students to learn by doing Includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples Can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists
Author: Sidharth Kankanala
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
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: Yingyao Hu
Publisher: International Monetary Fund
ISBN: 1498310753
Category : Business & Economics
Languages : en
Pages : 57
Get Book Here
Book Description
This paper seeks to illuminate the uncertainty in official GDP per capita measures using auxiliary data. Using satellite-recorded nighttime lights as an additional measurement of true GDP per capita, we provide a statistical framework, in which the error in official GDP per capita may depend on the country’s statistical capacity and the relationship between nighttime lights and true GDP per capita can be nonlinear and vary with geographic location. This paper uses recently developed results for measurement error models to identify and estimate the nonlinear relationship between nighttime lights and true GDP per capita and the nonparametric distribution of errors in official GDP per capita data. We then construct more precise and robust measures of GDP per capita using nighttime lights, official national accounts data, statistical capacity, and geographic locations. We find that GDP per capita measures are less precise for middle and low income countries and nighttime lights can play a bigger role in improving such measures.
Author: Naoya Sueishi
Publisher:
ISBN:
Category :
Languages : en
Pages : 145
Get Book Here
Book Description
Author: Huaiyu Xiong
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 210
Get Book Here
Book Description
In this work, we study a class of nonparametric/semiparametric structural models with endogeneity under a varying or partially varying coefficient representation for the regression function using instrumental variables. Under this representation, models are linear in the endogenous components with either unknown functional coefficients of the predetermined variables or constant coefficients. To estimate the functional coefficients in a nonparametric functional coefficient model, we propose a nonparametric two-step estimator that uses local linear approximations in both steps. The first step is to estimate a vector of reduced forms of regression models and the second step is a local linear regression using the estimated reduced forms as regressors. To efficiently estimate the parameters in the partially varying coefficient structural model, we first regard the constant coefficients as functional coefficients and then apply the above nonparametric two-step estimation procedure. The final estimators of those parameters are obtained by taking the average of all the estimates at each sample point. To estimate the functional coefficients, we simply use the partial residuals by removing the constant coefficients part and then apply the above proposed nonparametric two-step estimation procedure. The large sample results including the consistency and asymptotic normality of all the proposed estimators of functional /constant coefficients for both nonparametric and semiparametric models are derived and more importantly, it is demonstrated that the estimators of the parameters are [the square root of]n-consistent. Finally, both Monte Carlo simulation studies and an application are used to illustrate the performance of the finite sample properties.
Author: Wolfgang Karl Härdle
Publisher: Springer Science & Business Media
ISBN: 364217146X
Category : Mathematics
Languages : en
Pages : 317
Get Book Here
Book Description
The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Author: James Joseph Heckman
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 40
Get Book Here
Book Description
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.
