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2 edition of Inference on parameter sets in econometric models found in the catalog.

Inference on parameter sets in econometric models

by Victor Chernozhukov

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Published by Massachusetts Institute of Technology, Dept. of Economics in Cambridge, MA .
Written in English


About the Edition

This paper provides confidence regions for minima of an econometric criterion function Q([theta]). The minima form a set of parameters, [theta]I, called the identified set. In economic applications, [theta]I represents a class of economic models that are consistent with the data. Our inference procedures are criterion function based and so our confidence regions, which cover [theta]I with a prespecified probability, are appropriate level sets of Qn([theta]), the sample analog of Q([theta]). When [theta]I is a singleton, our confidence sets reduce to the conventional confidence regions based on inverting the likelihood or other criterion functions. We show that our procedure is valid under general yet simple conditions, and we provide feasible resampling procedure for implementing the approach in practice. We then show that these general conditions hold in a wide class of parametric econometric models. In order to verify the conditions, we develop methods of analyzing the asymptotic behavior of econometric criterion functions under set identification and also characterize the rates of convergence of the confidence regions to the identified set. We apply our methods to regressions with in terval data and set identified method of moments problems. We illustrate our methods in an empirical Monte Carlo study based on Current Population Survey data. Keywords: Set estimator, level sets, interval regression, subsampling bootsrap. JEL Classifications: C13, C14, C21, C41, C51, C53.

Edition Notes

StatementVictor Chernozhukov, Han Hong [and] Elie Tamer
SeriesWorking paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 06-18, Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 06-18.
ContributionsHong, Han, Tamer, Elie, Massachusetts Institute of Technology. Dept. of Economics
The Physical Object
Pagination43 p. :
Number of Pages43
ID Numbers
Open LibraryOL24644063M
OCLC/WorldCa143766083

HBM 15S D^WEY MassachusettsInstituteofTechnology DepartmentofEconomics WorkingPaperSeries ESTIMATIONANDCONFIDENCEREGIONSFORPARAMETER SETSINECONOMETRICMODELS. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): • Consider a population criterion function Q(θ) 0. An economic model θ ∈ Θ ⊂ R k passes empirical restrictions if Q(θ) = 0. Denote the set of parameters that pass these restrictions as ΘI. That is, ΘI = {θ ∈ Θ: Q(θ) = 0} = arg min Q(θ). θ∈Θ ΘI will be called the identified set.

The asymptotic properties of parameter estimators which are based on a model that has been selected by a model selection procedure are investigated. In particular, the asymptotic distribution is derived and the effects of the model selection process on subsequent inference are illustrated.   where A and B are events, P(A|B) is the conditional probability that event A occurs given that event B has already occurred (P(B|A) has the same meaning but with the roles of A and B reversed) and P(A) and P(B) are the marginal probabilities of event A and event B occurring respectively.. Example. Mathematical definitions can often feel too abstract and scary so let’s try to understand this.

in models that do not point identify a parameter. Therefore, new methods for inference are developed. These methods construct con-fidence sets for partially identified parameters, and confidence regions for sets of parameters, or identifiable sets. Review in Advance first posted online on Febru (C hanges may. Designed to bridge the gap between social science studies and field-econometrics, Econometric Analysis, 8th Edition, Global Edition, presents this ever-growing area at an accessible graduate level. The book first introduces students to basic techniques, a rich variety of models, and underlying theory that is easy to put into practice.


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Inference on parameter sets in econometric models by Victor Chernozhukov Download PDF EPUB FB2

Inference to econometric models that are set identifled, i.e., models where the objective function is minimized on a set of parameters, the identifled set. Our goal is to make inferences directly on the identifled set and to provide a method of obtaining confldence regions with good properties (such as.

"See recent revision called "Estimation & confidence regions for parameter sets in economic modes," [SSRN] id# " Includes bibliographical references (p. ) This paper provides confidence regions for minima of an econometric criterion function Q([mu]).

The minima form a set of parameters, [Theta]I, called the identified : The minima form a set of parameters, [theta]I, called the identified set. In economic applications, [theta]I represents a class of economic models that are consistent with the data.

Our inference procedures are criterion function based and so our confidence regions, which cover [theta]I with a prespecified probability, are appropriate level Pages: We provide new methods for inference in econometric models where the parameter of interest is a set.

These models arise in many situations where point identification requires strong (and sometimes. Request PDF | Inference on Identified Parameter Sets in Econometric Models | J -- Date: Original version: May This version: June, p. 1 | Find, read and cite all the. We provide new methods for inference in econometric models where the parameter of interest is a set.

These models arise in many situations where point identification requires strong (and sometimes untestable) assumptions. Every parameter vector in the set of interest represents a feasible economic model that generated the data.

Our point of departure is {\it set} $\Theta_I$ that minimizes a. rpartialidentificationproblemsariseinnonlinearmomentand instrumentalvariablesproblems,nko()andChernozhukovandHansen. This book is divided into four parts: identification and efficient estimation in econometrics; asymptotic approximations to the distributions of econometric estimators and tests; inference involving potentially nonstationarity in time series; and, finally, nonparametric and semiparametric inference.

This paper considers the problem of inference for partially identified econometric models. The class of models studied are defined by a population objective function Q (θ, P) for θ ∈ second argument indicates the dependence of the objective function on P, the distribution of the observed the classical extremum estimation framework, it is not assumed that Q (θ, P).

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This book is long overdue. HB31 i^tlW^Y.M MassachusettsInstituteofTechnology DepartmentofEconomics WorkingPaperSeries INFERENCEONPARAMETERSETSINECONOMETRICMODELS (Seerecentrevisioncalled.

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Econometric relations are often simultaneous in the sense that some of their variables are connected by a system of such equations. These variables are called endogenous in the system and the others, the values of which are supposed to be determined outside the system, the statistical analysis of such relations is based on time series, a distinction is also made between lagged and.

The aim of this book is to present the main statistical tools of econometrics. It covers almost all modern econometric methodology and unifies the approach by using a small number of estimation techniques, many from generalized method of moments (GMM) estimation. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol.

75(5), pagesSeptember. Charles F. Manski & Elie Tamer, " Inference on Regressions with Interval Data on a Regressor or Outcome," Econometrica, Econometric Society, vol.

70(2), pagesMarch. identified econometric models. A partially identified model is a model in which the parameter of interest is not uniquely defined by the distribution of the observed data. Such models arise naturally in many parts of empirical work in economics.

The class of models considered is defined by a population objective function Q(!,P) for. ∈ ". The. An econometric model then is a set of joint probability distributions to which the true joint probability distribution of the variables under study is supposed to belong.

In the case in which the elements of this set can be indexed by a finite number of real-valued parameters, the model is called a parametric model ; otherwise it is a.

Inference is essentially the process of creating a hypothesis of the parameters that describe a population by testing the sample parameters (such as ^ and ^) that we already have from a sample of the population.

For example, you have a sample of size N, and you have created a model that can be used to predict changes in the units of the sample. The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems.

It introduces the key results and ideas in an accessible, yet rigorous way. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference.

This paper provides computationally intensive, yet feasible methods for inference in a very general class of partially identified econometric models.

Let denote the distribution of the observed data. The class of models we consider is defined by a population objective function (,) for ∈.Offering students a unifying theoretical perspective, this innovative text emphasizes nonlinear techniques of estimation, including nonlinear least squares, nonlinear instrumental variables, maximum likelihood and the generalized method of moments, but nevertheless relies heavily on simplegeometrical arguments to develop intuition.

One theme of the book is the use of artificial regressions for. GARCH Models: Structure, Statistical Inference and Financial Applications, Second Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.