Advanced Econometrics

Advanced Econometrics Is Both A Comprehensive Text For Graduate Students And A Reference Work For Econometricians. It Will Also Be Valuable To Those Doing Statistical Analysis In The Other Social Sciences. Its Main Features Are A Thorough Treatment Of Cross-section Models, Including Qualitative Response Models, Censored And Truncated Regression Models, And Markov And Duration Models, As Well As A Rigorous Presentation Of Large Sample Theory, Classical Least-squares And Generalized Least-squares Theory, And Nonlinear Simultaneous Equation Models. Although The Treatment Is Mathematically Rigorous, The Author Has Employed The Theorem-proof Method With Simple, Intuitively Accessible Assumptions. This Enables Readers To Understand The Basic Structure Of Each Theorem And To Generalize It For Themselves Depending On Their Needs And Abilities. Many Simple Applications Of Theorems Are Given Either In The Form Of Examples In The Text Or As Exercises At The End Of Each Chapter In Order To Demonstrate Their Essential Points.--publisher's Website. 1. Classical Least Squares Theory -- 2. Recent Developments In Regression Analysis -- 3. Large Sample Theory -- 4. Asymptotic Properties Of Extremum Estimators -- 5. Time Series Analysis -- 6. Generalized Least Squares Theory -- 7. Linear Simultaneous Equations Models -- 8. Nonlinear Simultaneous Equations Models -- 9. Qualitative Response Models -- 10. Tobit Models -- 11. Markov Chain And Duration Models -- Appendix 1. Useful Theorems In Matrix Analysis -- Appendix 2. Distribution Theory. Takeshi Amemiya. Includes Indexes. Bibliography: P. [475]-504. Advanced Econometrics is both a comprehensive text for graduate students and a reference work for econometricians. It will also be valuable to those doing statistical analysis in the other social sciences. Its main features are a thorough treatment of cross-section models, including qualitative response models, censored and truncated regression models, and Markov and duration models, as well as a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models. Although the treatment is mathematically rigorous, the author has employed the theorem-proof method with simple, intuitively accessible assumptions. This enables readers to understand the basic structure of each theorem and to generalize it for themselves depending on their needs and abilities. Many simple applications of theorems are given either in the form of examples in the text or as exercises at the end of each chapter in order to demonstrate their essential points. ([source][1]) [1]: http://www.hup.harvard.edu/catalog.php?isbn=9780674005600 Cover......Page 1 Preface......Page 7 Contents......Page 9 1. Classical Least Squares Theory......Page 11 2. Recent Developments in Regression Analysis......Page 55 3. Large Sample Theory......Page 91 4. Asymptotic Properties of Extremum Estimators......Page 115 5. Time Series Analysis......Page 169 6. Generalized Least Squares Theory......Page 191 7. Linear Simultaneous Equations Models......Page 238 8. Nonlinear Simultaneous Euations Models......Page 255 9. Qualitative Response Models......Page 277 10. Tobit Models......Page 370 11. Markov Chain and Duration Models......Page 422 Appendix 1: Useful Theorems in Matrix Analysis......Page 469 Appendix 2: Distribution Theory......Page 473 Notes......Page 475 References......Page 485 Name Index......Page 516 Subject Index......Page 522 1. Classical Least Squares Theory 2. Recent Developments in Regression Analysis 3. Large Sample Theory 4. Asymptotic Properties of Extremum Estimators 5. Time Series Analysis 6. Generalized Least Squares Theory 7. Linear Simultaneous Equations Models 8. Nonlinear Simultaneous Equations Models 9. Qualitative Response Models 10. Tobit Models 11. Markov Chain and Duration Models Appendix 1. Useful Theorems in Matrix Analysis Appendix 2. Distribution Theory Notes References Name Index Subject Index The main features of this text are a thorough treatment of cross-section models-including qualitative response models, censored and truncated regression models, and Markov and duration models-and a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models.