This text provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation. Econometric Theory and Methods is designed for beginning graduate courses. The book is suitable for both one- and two-term courses at the Masters or Ph.D. level. It can also be used in a final-year undergraduate course for students with sufficient backgrounds in mathematics and statistics.
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RUSSELL DAVIDSON holds the Canada Research Chair in Econometrics at McGill University in Montreal. He also teaches at GREQAM in Marseille and previously taught for many years at Queen's University. He has a Ph.D. in Physics from the University of Glasgow and a Ph.D. in Economics from the University of British Columbia. Professor Davidson is a Fellow of the Econometric Society and the author of many scientific papers. He is the coauthor of Estimation and Inference in Econometrics (OUP, 1993). JAMES G. MACKINNON is the Sir Edward Peacock Professor of Econometrics and Head of the Department at Queen's University in Kingston, Ontario, Canada, where he has taught since obtaining his Ph.D. from Princeton University in 1975. He is a Fellow of the Econometric Society and of the Royal Society of Canada and a past President of the Canadian Economics Association (2001-2002). Professor MacKinnon has written more than seventy journal articles and book chapters, and he is the coauthor of Estimation and Inference in Econometrics (OUP, 1993).
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