The Econometric Analysis of Time SeriesMIT Press, 1990 - 387 pagina's This new edition of A.C. Harvey's clearly written, upper-level text has been revised and several sections have been completely rewritten. There is new material on a number of topics, including unit roots, ARCH, and cointegration. The Econometric Analysis of Time Series focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs. It explores the way in which recent advances in time series analysis have affected the development of a theory of dynamic econometrics, sets out an integrated approach to the problems of estimation and testing based on the method of maximum likelihood, and presents a coherent strategy for model selection. A.C. Harvey is Professor of Econometrics at the London School of Economics. |
Inhoudsopgave
Introduction | 1 |
3 | 80 |
Numerical Optimisation | 122 |
Test Procedures and Model Selection | 146 |
Regression Models with Serially Correlated Disturbances | 191 |
Dynamic Models I | 225 |
Stochastic Difference Equations | 264 |
Simultaneous Equation Models | 313 |
Appendix on Matrix Algebra | 359 |
Answers to Selected Exercises | 369 |
| 380 | |
| 386 | |
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algorithm alternative ARMA process assumption asymptotic distribution asymptotically efficient autocorrelation autoregressive B₁ Cochrane-Orcutt computed condition Consider consistent estimator constructed covariance matrix defined denotes dependent variable derivatives distributed lag disturbance term dynamic model econometric econometric model endogenous estimator of ẞ example exogenous variables explanatory variables expression F-distribution Gauss-Newton generalisation given H₁ heteroscedasticity identifiability information matrix instrumental variables iterative lag operator large samples least squares likelihood function linear regression LM test log L(V maximising mean minimising ML estimator multiplier non-linear normally distributed null hypothesis observations obtained OLS residuals optimisation parameters plim polynomial prediction errors problem properties recursive residuals reduced form regressors restrictions result Section serial correlation series model small samples specification stochastic difference equation sum of squares test procedure test statistic transfer function two-step estimator u₁ uncorrelated unrestricted model variance vector Wald Wald test white noise x₁ y₁ yields