Forecasting GDP with aggregated and sectoral data

Hassani, Hossein, Soofi, Abdol and Avazalipour, Mohammad Sadegh 2011. Forecasting GDP with aggregated and sectoral data. Fluctuation and Noise Letters 10 (3) , pp. 249-265. 10.1142/S0219477511000533

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Abstract

We use the Singular Spectrum Analysis (SSA), a forecasting method which is based on the noise reduction procedure, in prediction of the Iranian gross domestic product (GDP). Two different approaches are considered in forecasting the series. In the first approach, we apply SSA to the aggregate GDP series. In the second approach, we predict the GDP by first forecasting the GDP of the sectors of the economy, and then sum the predicted values as the forecast of the aggregate GDP. We measured the prediction accuracy of both approaches using various criteria, and found that predictions based on the disaggregated, sectoral GDP tend to outperform the predictions based on the aggregated data.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Uncontrolled Keywords:	Singular Spectrum Analysis; noise reduction; GDP forecasting; decomposition; aggregated and sectoral data
Publisher:	World Scientific Publishing
ISSN:	0219-4775
Last Modified:	19 Mar 2016 22:52
URI:	https://orca.cardiff.ac.uk/id/eprint/29772

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