“Forecasting Business surveys indicators: neural networks vs. time series models”
AbstractThe objective of this paper is to compare different forecasting methods for the short run forecasting of Business Survey Indicators. We compare the forecasting accuracy of Artificial Neural Networks (ANN) vs. three different time series models: autoregressions (AR), autoregressive integrated moving average (ARIMA) and self-exciting threshold autoregressions (SETAR). We consider all the indicators of the question related to a country’s general situation regarding overall economy, capital expenditures and private consumption (present judgement, compared to same time last year, expected situation by the end of the next six months) of the World Economic Survey (WES) carried out by the Ifo Institute for Economic Research in co-operation with the International Chamber of Commerce. The forecast competition is undertaken for fourteen countries of the European Union. The main results of the forecast competition are offered for raw data for the period ranging from 1989 to 2008, using the last eight quarters for comparing the forecasting accuracy of the different techniques. ANN and ARIMA models outperform SETAR and AR models. Enlarging the observed time series of Business Survey Indicators is of upmost importance in order of assessing the implications of the current situation and its use as input in quantitative forecast models.
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Bibliographic InfoPaper provided by University of Barcelona, Regional Quantitative Analysis Group in its series AQR Working Papers with number 201312.
Length: 28 pages
Date of creation: Nov 2013
Date of revision: Nov 2013
Business surveys; Forecasting; Time series models; Nonlinear models; Neural networks.;
Other versions of this item:
- Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," IREA Working Papers 201320, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
- NEP-ALL-2013-11-29 (All new papers)
- NEP-FOR-2013-11-29 (Forecasting)
- NEP-ORE-2013-11-29 (Operations Research)
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