Identification and Inference for Econometric Models

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.

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.