Young, Peter C. (1994) Time-variable parameter and trend estimation in non-stationary economic time series. Journal of Forecasting, 13 (2). pp. 179-210. ISSN 1099-131XFull text not available from this repository.
The paper describes a general approach to the modelling of nonlinear and nonstationary economic systems from time-series data. This method exploits recursive state space filtering and fixed interval smoothing algorithms to decompose the time-series into long term trend and short term small perturbational components, each of which are then modelled by linear stochastic models which may be characterised by time variable parameters. The approach is illustrated by an example which explores the relationship between the variations in quarterly GNP and Unemployment in the USA over the period 1948 to 1988 and questions previous claims about the changes in the slope of the long term trend in loge(GNP) over this same period. The paper also points out that the recursive approach to estimation facilitates the use of these methods in the development of adaptive forecasting and control systems.
|Journal or Publication Title:||Journal of Forecasting|
|Uncontrolled Keywords:||Recursive estimation • Fixed interval smoothing • Linearisation • Unobserved components • Long term trends • Small perturbations • Time variable parameters • Instrumental variable estimation • GNP-Unemployment in USA • Adaptive forecasting and control|
|Subjects:||?? ge ??|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
|Deposited On:||23 Jan 2009 15:14|
|Last Modified:||23 Mar 2017 17:19|
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