Econometric Modeling and Inference

Jean-Pierre Florens is Professor of Mathematics at the University of Toulouse I, where he holds the Chair in Statistics and Econometrics, and a senior member of the Institut Universitaire de France. He is also a member of the IDEI and GREMAQ research groups. Professor Florens' research interests include: statistics and econometrics methods, applied econometrics, and applied statistics. He is coauthor of Elements of Bayesian Statistics with Michel Mouchart and Jean-Marie Rolin (1990). The editor or co-editor of several econometrics and statistics books, he has also published numerous articles in the major econometric reviews, such as Econometrica, Journal of Econometrics, and Econometric Theory. Vêlayoudom Marimoutou is Professor of Economics at the University of Aix-Marseille 2 and a member of GREQAM. His research fields include: time series analysis, non-stationary processes, long range dependence, and applied econometrics of exchange rates, finance, macroeconometrics, convergence, and international trade. His articles have appeared in publications such as the Journal of International Money and Finance, Oxford Bulletin of Economics and Statistics, and the Journal of Applied Probability. Anne Peguin-Feissolle is Research Director of the National Center of Scientific Research (CNRS) and a member of the GREQAM. She conducts research on econometric modelling, especially nonlinear econometrics, applications to macroeconomics, finance, spatial economics, artificial neural network modelling, and long memory problems. Professor Peguin-Feissolle's published research has appeared in Economics Letters, Economic Modelling, European Economic Review, Applied Economics, and the Annales d'Economie et de Statistique, among other publications.

Part I. Statistical Methods: 1. Statistical models; 2. Sequential models and asymptotics; 3. Estimation by maximization and by the method of moments; 4. Asymptotic tests; 5. Nonparametric methods; 6. Simulation methods; Part II. Regression Models: 7. Conditional expectation; 8. Univariate regression; 9. Generalized least squares method, heteroskedasticity, and multivariate regression; 10. Nonparametric estimation of the regression; 11. Discrete variables and partially observed models; Part III. Dynamic Models: 12. Stationary dynamic models; 13. Nonstationary processes and cointegration; 14. Models for conditional variance; 15. Nonlinear dynamic models; Part IV. Structural Modeling: 16. Identification and over identification in structural modeling; 17. Simultaneity; 18. Models with unobservable variables.