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Time-variable parameter and trend estimation in non-stationary economic time series.

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Time-variable parameter and trend estimation in non-stationary economic time series. / Young, Peter C.

In: Journal of Forecasting, Vol. 13, No. 2, 1994, p. 179-210.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Young, Peter C. / Time-variable parameter and trend estimation in non-stationary economic time series. In: Journal of Forecasting. 1994 ; Vol. 13, No. 2. pp. 179-210.

Bibtex

@article{494580f836364a9dae3da986d1f6aacb,
title = "Time-variable parameter and trend estimation in non-stationary economic time series.",
abstract = "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.",
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",
author = "Young, {Peter C.}",
year = "1994",
doi = "10.1002/for.3980130210",
language = "English",
volume = "13",
pages = "179--210",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Time-variable parameter and trend estimation in non-stationary economic time series.

AU - Young, Peter C.

PY - 1994

Y1 - 1994

N2 - 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.

AB - 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.

KW - 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

U2 - 10.1002/for.3980130210

DO - 10.1002/for.3980130210

M3 - Journal article

VL - 13

SP - 179

EP - 210

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 2

ER -