Statistical Foundations of Econometric Modelling
This textbook provides an introduction to econometrics through a grounding in probability theory and statistical inference. The emphasis is on the concepts and ideas underlying probability theory and statistical inference, and on motivating the learning of them both at a formal and an intuitive level. It encourages the mastering of fundamental concepts and theoretical perspectives which guide the formulation and solution of problems in econometric modelling. This makes it an ideal introduction to empirical econometric modelling and the more advanced econometric literature. It is recommended for use on courses giving students a thorough grounding in econometrics at undergraduate or graduate level.
- Electronic book text
- 11 May 2012
- CAMBRIDGE UNIVERSITY PRESS
- Cambridge University Press (Virtual Publishing)
- Cambridge, United Kingdom
- 70 b/w illus. 12 tables 97 exercises
Table of contents
Foreword David Hendry; Preface; Acknowledgements; Part I. Introduction: 1. Econometric modelling, a preliminary view; 2. Descriptive study of data; Part II. Probability Theory: 3. Probability; 4. Random variables and probability distributions; 5. Random vectors and their distributions; 6. Functions of random variables; 7. The general notion of expectation; 8. Stochastic processes; 9. Limit theorems; 10. Introduction to asymptotic theory; Part III. Statistical Inferences: 11. The nature of statistical inference; 12. Estimation I - properties of estimators; 13. Estimation II - methods; 14. Hypothesis testing and confidence regions; 15. The multivariate normal distribution; 16. Asymptotic test procedures; Part IV. The Linear Regression and Related Statistical Models: 17. Statistical models in econometrics; 18. The Gauss linear model; 19. The linear regression model I - specification, estimation and testing; 20. the linear regression model II - departures from the assumptions underlying the statistical GM; 21. The linear regression model III- departures from the assumptions underlying the probability model; 22. The linear regression model IV - departures from the sampling model assumption; 23. The dynamic linear regression model; 24. The multivariate linear regression model; 25. The simultaneous equations model; 26. Epilogue: towards a methodology of econometric modelling; References; Index.