Forecasting Economic Time Series With Structural and Box-Jenkins Models: A Case Study
Journal of Business and Economic StatisticsPublished 1 October 1983
Andrew Harvey, P. H. J. Todd
Citations272
SJR quartileQ1
SJR score4.17
SNIP2.29
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
The basic structural model is a univariate time series model consisting of a slowly changing trend component, a slowly changing seasonal component, and a random irregular component. It is part of a class of models that have a number of advantages over the seasonal ARIMA models adopted by Box and Jenkins (1976). This article reports the results of an exercise in which the basic structural model was estimated for six U.K. macroeconomic time series and the forecasting performance compared with that of ARIMA models previously fitted by Prothero and Wallis (1976).
Keywords
Decision SciencesEconomics, Econometrics and Finance
Journal of Monetary EconomicsTrends and random walks in macroeconmic time series
4,635 Citations1982Charles R. Nelson, Charles R. Plosser
The Economic JournalEconometric Modelling of the Aggregate Time-Series Relationship Between Consumers' Expenditure and Income in the United Kingdom
1,623 Citations1978James Davidson, David F. Hendry +2 more
Journal of ForecastingThe accuracy of extrapolation (time series) methods: Results of a forecasting competition
1,469 Citations1982Spyros Makridakis, A. Andersen +7 more
The results of a forecasting competition are presented to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.
Journal of the Royal Statistical Society Series B (Statistical Methodology)Bayesian Forecasting
650 Citations1976P. J. Harrison, C. F. Stevens
Journal of the American Statistical AssociationAn ARIMA-Model-Based Approach to Seasonal Adjustment
453 Citations1982Steven C. Hillmer, George C. Tiao
Journal of the American Statistical AssociationA One-Factor Multivariate Time Series Model of Metropolitan Wage Rates
447 Citations1981Robert F. Engle, Mark W. Watson
Journal of ForecastingA unified view of statistical forecasting procedures
246 Citations1984Andrew Harvey
This paper sets out to show the relationship between various procedures for univariate time series forecasting by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components.
EconometricaTesting for Fourth Order Autocorrelation in Quarterly Regression Equations
175 Citations1972Kenneth F. Wallis
Journal of Time Series AnalysisA NONSTATIONARY TIME SERIES MODEL AND ITS FITTING BY A RECURSIVE FILTER
131 Citations1981Genshiro Kitagawa
EconometricaMaximum Likelihood Estimation of Regression Models with First Order Moving Average Errors when the Root Lies on the Unit Circle
124 Citations1983J. D. Sargan, Alok Bhargava
Journal of the Royal Statistical Society Series C (Applied Statistics)Comparison of Forecast and Actuality
104 Citations1976George E. P. Box, George C. Tiao
Journal of ForecastingSome practical aspects of forecasting in organizations
94 Citations1982Gwilym M. Jenkins
The Annals of StatisticsSignal Extraction Error in Nonstationary Time Series
80 Citations1979David A. Pierce
Journal of Time Series AnalysisFINITE SAMPLE PREDICTION AND OVERDIFFERENCING
26 Citations1981Andrew Harvey
