Identification and Inference for Econometric Models (eBook, PDF)

Essays in Honor of Thomas Rothenberg
Redaktion: Andrews, Donald W. K.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 4. Juli 2005
• Englisch
• ISBN-13: 9780511114045
• Artikelnr.: 38196398

Autorenporträt

Donald W. K. Andrews is the William K. Lanman Jr. Professor of Economics in the Department of Economics at Yale University. The author of more than 70 professional publications, Professor Andrews is a Fellow of the Econometric Society, former co-editor of the journal Econometric Theory, and is a Fellow of the Journal of Econometrics. He did his graduate work at the University of California, Berkeley, where he obtained an MA in Statistics and a PhD from the Economics Department under the supervision of Peter J. Bickel and Thomas J. Rothenberg.
James H. Stock is Professor of Economics in the Department of Economics at Harvard University. Previously he was the Roy E. Larson Professor of Political Economy at the Kennedy School of Government, Harvard, and Professor of Economics at the University of California, Berkeley. He has written more than 90 professional publications, including a popular undergraduate econometrics textbook (co-authored by Mark Watson). He is a Fellow of the Econometric Society, the former chair of the Board of Editors of The Review of Economics and Statistics, and is a Research Associate of the National Bureau of Economic Research. Stock did his graduate work at the University of California, Berkeley, where he obtained an MA in Statistics and a PhD from the Economics Department under the supervision of Thomas J. Rothenberg.

Inhaltsangabe

Part I. Identification and Efficient Estimation: 1. Incredible structural
inference Thomas J. Rothenberg; 2. Structural equation models in human
behavior genetics Arthur S. Goldberger; 3. Unobserved heterogeneity and
estimation of average partial effects Jeffrey M. Wooldridge; 4. On
specifying graphical models for causation and the identification problem
David A. Freedman; 5. Testing for weak instruments in linear IV regression
James H. Stock and Motohiro Yogo; 6. Asymptotic distributions of
instrumental variables statistics with many instruments James H. Stock and
Motohiro Yogo; 7. Identifying a source of financial volatility Douglas G.
Steigerwald and Richard J. Vagnoni; Part II. Asymptotic Approximations: 8.
Asymptotic expansions for some semiparametric program evaluation estimators
Hidehiko Ichimura and Oliver Linton; 9. Higher-order improvements of the
parametric bootstrap for Markov processes Donald W. K. Andrews; 10. The
performance of empirical likelihood and its generalizations Guido W. Imbens
and Richard H. Spady; 11. Asymptotic bias for GMM and GEL estimators with
estimated nuisance parameters Whitney K. Newey, Joaquim J. S. Ramalho and
Richard J. Smith; 12. Empirical evidence concerning the finite sample
performance of EL-type structural equation estimation and inference methods
Ron C. Mittelhammer, George G. Judge and Ron Schoenberg; 13. How accurate
is the asymptotic approximation to the distribution of realised variance?
Ole E. Barndorff-Nielsen and Neil Shephard; 14. Testing the semiparametric
Box-Cox model with the bootstrap N. E. Savin and Allan H. Wurtz; Part III.
Inference Involving Potentially Nonstationary Time Series: 15. Tests of the
null hypothesis of cointegration based on efficient tests for a unit MA
root Michael Jansson; 16. Robust confidence intervals for autoregressive
coefficients near one Samuel B. Thompson; 17. A unified approach to testing
for stationarity and unit roots Andrew C. Harvey; 18. A new look at panel
testing of stationarity and the PPP hypothesis Jushan Bai and Serena Ng;
19. Testing for unit roots in panel data: an exploration using real and
simulated data Brownwyn H. Hall and Jacques Mairesse; 20. Forecasting in
the presence of structural breaks and policy regime shifts David F. Hendry
and Grayham E. Mizon; Part IV. Nonparametric and Semiparametric Inference:
21. Nonparametric testing of an exclusion restriction Peter J. Bickel,
Ya'acov Ritov and James L. Powell; 22. Pairwise difference estimators for
nonlinear models Bo E. Honoré and James L. Powell; 23. Density weighted
linear least squares Whitney K. Newey and Paul A. Ruud.