UNSPECIFIED (1996) Forecasting consumers' expenditure: A comparison between econometric and neural network models. INTERNATIONAL JOURNAL OF FORECASTING, 12 (2). pp. 255-267. ISSN 0169-2070

Full text not available from this repository.

Abstract

This paper is motivated by the difficulties faced by forecasters in predicting the decline in the growth rate of consumers': expenditure in the late 1980s. The econometric specifications of four competing explanations are replicated and the static forecasts compared with the actual outturns. The same data are then used to estimate neural network models. The main issue is whether the neural network technology can extract any more from the data sets provided than the econometric approach. It is found that the neural network models describe the decline in the growth of consumption since the late 1980s as well as, but no better than, the econometric specifications included in the exercise, and are shown to be robust when faced with a small number of data points. However, whichever approach is adopted, it is the skill of choosing the menu of explanatory variables which determines the success of the final results.

Item Type:	Journal Article
Subjects:	H Social Sciences > HC Economic History and Conditions H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Journal or Publication Title:	INTERNATIONAL JOURNAL OF FORECASTING
Publisher:	ELSEVIER SCIENCE BV
ISSN:	0169-2070
Date:	June 1996
Volume:	12
Number:	2
Number of Pages:	13
Page Range:	pp. 255-267
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/18616

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item