Unobserved components and time series econometrics / [edited by] Siem Jan Koopman and Neil Shephard.

Includes bibliographical references and index.

1. Introduction ;
2. The Development of a Time Series Methodology: from Recursive Residuals to Dynamic Conditional Score Models ;
3. A State-Dependent Model for Inflation Forecasting ;
4. Measuring the Tracking Error of Exchange Traded Funds ;
5. Measuring the Dynamics of Global Business Cycle Connectedness ;
6. Inferring and Predicting Global Temperature Trends ;
7. Forecasting the Boat Race ;
8. Tests for Serial Dependence in Static, Non-Gaussian Factor Models ;
9. Inference for Models with Asymmetric alpha-Stable Noise Processes ;
10. Martingale Unobserved Component Models ;
11. More is Not Always Better: Kalman Filtering in Dynamic Factor Models ;
12. On Detecting End-of-Sample Instabilities ;
13. Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation ;
14. The Superiority of the LM Test in a Class of Econometric Models Where the Wald Test Performs Poorly ;
15. Generalised Linear Spectral Models.

This volume presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives. It also presents empirical studies where the UC time series methodology is adopted. Drawing on the intellectual influence of Andrew Harvey, the work covers three main topics: the theory and methodology for unobserved components time series models; applications of unobserved components time series models; and time series econometrics and estimation and testing. These types of time series models have seen wide application in economics, statistics, finance, climate change, engineering, biostatistics, and sports statistics. The volume effectively provides a key review into relevant research directions for UC time series econometrics and will be of interest to econometricians, time series statisticians, and practitioners (government, central banks, business) in time series analysis and forecasting, as well to researchers and graduate students in statistics, econometrics, and engineering.