Unobserved Components and Time Series Econometrics
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Product details:
- Publisher OUP Oxford
- Date of Publication 19 November 2015
- ISBN 9780199683666
- Binding Hardback
- No. of pages390 pages
- Size 240x174x30 mm
- Weight 778 g
- Language English
- Illustrations 88 Figures and 44 Tables 0
Categories
Short description:
Presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives.
MoreLong description:
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:
Introduction
The Development of a Time Series Methodology: from Recursive Residuals to Dynamic Conditional Score Models
A State-Dependent Model for Inflation Forecasting
Measuring the Tracking Error of Exchange Traded Funds
Measuring the Dynamics of Global Business Cycle Connectedness
Inferring and Predicting Global Temperature Trends
Forecasting the Boat Race
Tests for Serial Dependence in Static, Non-Gaussian Factor Models
Inference for Models with Asymmetric ?-Stable Noise Processes
Martingale Unobserved Component Models
More is Not Always Better: Kalman Filtering in Dynamic Factor Models
On Detecting End-of-Sample Instabilities
Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation
The Superiority of the LM Test in a Class of Econometric Models Where the Wald Test Performs Poorly
Generalised Linear Spectral Models
