Forecasting composite indicators with anticipated information: an application to the industrial production index
Battaglia, F; Fenga, L
Date: 24 June 2003
Article
Journal
Journal of the Royal Statistical Society Series C (Applied Statistics)
Publisher
Wiley / Royal Statistical Society
Publisher DOI
Abstract
Many economic and social phenomena are measured by composite indicators
computed as weighted averages of a set of elementary time series. Often data are collected
by means of large sample surveys, and processing takes a long time, whereas the values of
some elementary component series may be available some time before the others, ...
Many economic and social phenomena are measured by composite indicators
computed as weighted averages of a set of elementary time series. Often data are collected
by means of large sample surveys, and processing takes a long time, whereas the values of
some elementary component series may be available some time before the others, and may
be used for forecasting the composite index. This problem is addressed within the framework
of prediction theory for stochastic processes. A method is proposed for exploiting anticipated
information in order to minimise the mean square forecast error, and for selecting the most
useful elementary series. An application to the Italian general industrial production index is
illustrated, which demonstrates that knowledge of anticipated values of some, or even just
one, component series may reduce the forecast error considerably.
Management
Faculty of Environment, Science and Economy
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