Forecasting Business surveys indicators: neural networks vs. time series models

Oscar Claveria and Salvador Torra ()
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Salvador Torra: Faculty of Economics, University of Barcelona

No 201312, AQR Working Papers from University of Barcelona, Regional Quantitative Analysis Group

Abstract: The 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.

Keywords: Business surveys; Forecasting; Time series models; Nonlinear models; Neural networks. (search for similar items in EconPapers)
Pages: 28 pages
Date: 2013-11, Revised 2013-11
New Economics Papers: this item is included in nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Forecasting Business surveys indicators: neural networks vs. time series models (2013)
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