Concisely written and up-to-date, this book provides a unified and comprehensive analysis of the full range of topics that comprise modern time series econometrics. While it does demand a good quantitative grounding, it does not require a high mathematical rigor or a deep knowledge of economics. One of the book's most attractive features is the close attention it pays throughout to economic models and phenomena. The authors provide a sound analysis of the statistical origins of topics such as seasonal adjustment, causality, exogeneity, cointegration, prediction, and forecasting. Their treatment of Box-Jenkins models and the Kalman filter represents a synthesis of the most recent theoretical and applied work in these areas.

Table of Contents

Preface; 1. Introduction; Part I. Traditional Methods: 2. Linear regression for seasonal adjustment; 3. Moving averages for seasonal adjustment; 4. Exponential smoothing methods; Part II. Probabilistic and Statistical Properties of Stationary Processes: 5. Some results on the univariate processes; 6. The Box and Jenkins method for forecasting; 7. Multivariate time series; 8. Time-series representations; 9. Estimation and testing (stationary case); Part III. Time-series Econometrics: Stationary and Nonstationary Models: 10. Causality, exogeneity, and shocks; 11. Trend components; 12. Expectations; 13. Specification analysis; 14. Statistical properties of nonstationary processes; Part IV. State-space Models: 15. State-space models and the Kalman filter; 16. Applications of the state-space model; References; Tables; Index.