Statistical Foundations of Econometric Modelling

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.