Inference for Partially Identified Econometric Models

Inference for Partially Identified Econometric Models PDF Author: Azeem M. Shaikh
Publisher:
ISBN:
Category :
Languages : en
Pages : 194

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Inference for Partially Identified Econometric Models

Inference for Partially Identified Econometric Models PDF Author: Azeem M. Shaikh
Publisher:
ISBN:
Category :
Languages : en
Pages : 194

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Inference for the Identified Set in Partially Identified Econometric Models

Inference for the Identified Set in Partially Identified Econometric Models PDF Author: Joseph P. Romano
Publisher:
ISBN:
Category :
Languages : en
Pages : 41

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Inference for Identifiable Parameters in Partially Identified Econometric Models

Inference for Identifiable Parameters in Partially Identified Econometric Models PDF Author: Joseph P. Romano
Publisher:
ISBN:
Category :
Languages : en
Pages : 39

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Inference for Partially Identified Economic Models

Inference for Partially Identified Economic Models PDF Author: Hiroaki Kaido
Publisher:
ISBN: 9781124030555
Category : Capital assets pricing model
Languages : en
Pages : 402

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When a sample of data does not fully reveal the "true" data generating structure (or parameter) but gives information that bounds the set of observationally equivalent structures, an economic model is said to be partially identified. This dissertation develops and applies estimation and inference methods for economic models whose population features are only partially identified. In Chapter 1 (co-authored with Halbert White), I apply econometric techniques from the partial identification literature to address a fundamental problem in asset pricing theory. Namely, that the market price of risk is only identified as a set under incomplete markets. I construct a set estimator and confidence regions for the set of market risk prices. I further show that it is possible to test hypotheses of economic interest without fully identifying the market price of risk. The econometric techniques used in Chapter 1 are developed by Chapter 2 (co-authored with Halbert White). When the dimension of the parameter space is large, this is a particular challenge for set-valued estimators, as high dimensionality can create computational difficulties and seriously hamper the interpretation of estimation results. We study how the use of a natural two-stage extension of the Chernozhukov, Hong, and Tamer's (2007) (CHT) framework can exploit a priori knowledge about the data generating process to mitigate the problems otherwise associated with set estimation in high-dimensional parameter spaces. Chapter 3 unifies two general approaches recently proposed in the literature, the criterion function approach and support function approach. CHT develop a theory of set estimation and inference for the set [Theta]/I of parameter values that minimize a criterion function. The support function approach provides an alternative characterization of CHT's level-set estimator by its supporting hyperplanes. This results in an estimation and inference method that has the wide applicability of the criterion function approach and the computational tractability of the support function approach. By establishing the asymptotic distribution of the properly normalized support function of the level set estimator, I provide Wald-type inference tools to conduct tests regarding the identified set [Theta]/I and a point [Theta]0 in the identified set.

Identification and Inference for Econometric Models

Identification and Inference for Econometric Models PDF Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 9780521844413
Category : Business & Economics
Languages : en
Pages : 606

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Book Description
This 2005 collection pushed forward the research frontier in four areas of theoretical econometrics.

Identification and Inference for Econometric Models

Identification and Inference for Econometric Models PDF Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 1139444603
Category : Business & Economics
Languages : en
Pages : 589

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Book Description
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. 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. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Microeconometrics

Microeconometrics PDF Author: Steven Durlauf
Publisher: Springer
ISBN: 0230280811
Category : Literary Criticism
Languages : en
Pages : 365

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Book Description
Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.

Inference in Partially Identified Models

Inference in Partially Identified Models PDF Author: Herman B. Leonard
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Partial Identification of Probability Distributions

Partial Identification of Probability Distributions PDF Author: Charles F. Manski
Publisher: Springer Science & Business Media
ISBN: 038721786X
Category : Mathematics
Languages : en
Pages : 188

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Book Description
The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. There is an enormous scope for fruitful inference using data and assumptions that partially identify population parameters.

Inference in Partially Identified Models with Many Moment Inequalities Using Lasso

Inference in Partially Identified Models with Many Moment Inequalities Using Lasso PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

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