Unobserved Components and Time Series Econometrics

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.

Table of Contents:
1: Siem Jan Koopman and Neil Shephard: Introduction 2: Andrew Harvey: The Development of a Time Series Methodology: from Recursive Residuals to Dynamic Conditional Score Models 3: Andrea Stella and James H. Stock: A State-Dependent Model for Inflation Forecasting 4: Giuliano De Rossi: Measuring the Tracking Error of Exchange Traded Funds 5: Francis X. Diebold and Kamil Yilmaz: Measuring the Dynamics of Global Business Cycle Connectedness 6: Craig Ansley and Piet de Jong: Inferring and Predicting Global Temperature Trends 7: Geert Mesters and Siem Jan Koopman: Forecasting the Boat Race 8: Gabriele Fiorentini and Enrique Sentana: Tests for Serial Dependence in Static, Non-Gaussian Factor Models 9: Tatjana Lemke and Simon J. Godsill: Inference for Models with Asymmetric *a-Stable Noise Processes 10: Neil Shephard: Martingale Unobserved Component Models 11: Pilar Poncela and Esther Ruiz: More is Not Always Better: Kalman Filtering in Dynamic Factor Models 12: Fabio Busetti: On Detecting End-of-Sample Instabilities 13: Jouni Helske and Jukka Nyblom: Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation 14: Jun Ma and Charles R. Nelson: The Superiority of the LM Test in a Class of Econometric Models Where the Wald Test Performs Poorly 15: Tommaso Proietti and Alessandra Luati: Generalised Linear Spectral Models

About the Author :
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.