Identification and Inference for Econometric Models - Essays in Honor of Thomas Rothenberg

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. Contents
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, Yaacov Ritov and James L. Powell
22. Pairwise difference estimators for nonlinear models Bo E. Honor and James L. Powell
23. Density weighted linear least squares Whitney K. Newey and Paul A. Ruud.

Author/Editor Details
Edited by: Donald W. K. Andrews, Yale University, Connecticut
Edited by: James H. Stock, Harvard University, Massachusetts