Unobserved Components and Time Series Econometrics

Siem Jan Koopman is a Professor of Econometrics at the VU University Amsterdam and Research Fellow at the Tinbergen Institute. Furthermore, he is a Visiting Professor at CREATES, University of Aarhus and a Visiting Researcher at the European Central Bank, Financial Research. He has held positions at LSE and Tilburg University, and has been a Research Fellow at the US Bureau of the Census, Washington DC, and a Fernand Braudel Senior Fellow at the European University Institute, Florence. Neil Shephard is Professor of Economics and of Statistics at Harvard University. He previously was a faculty member at the LSE and Oxford University. He was elected a Fellow of the Econometric Society in 2004 and a Fellow of the British Academy in 2006. He received an honourary doctorate in economics from Aarhus University in 2009. He was award the Richard Stone Prize in Applied Econometrics in 2012. He has been an associate editor of the academic journal Econometrica since 2002. He has previously been on the editorial boards of, for example, Review of Economic Studies, Biometrika and JRSSB.

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