Nowcasting emerging market's GDP: the importance of dimension reduction techniques

• Published in: Applied economics letters Vol. 26; no. 20; pp. 1670 - 1674
• Main Authors: Cepni, Oguzhan, Guney, I. Ethem
• Format: Journal Article
• Language: English
• Published: London Routledge 28-11-2019; Taylor & Francis LLC
• Subjects: Diffusion index; Economic fluctuations; Economic models; Economic theory; Economics; Emerging markets; Extraction; factor extraction methods; factor model; Finance; forecasting; GDP; Gross Domestic Product; Prices; Principal components analysis; Specification; Usefulness
• ISSN: 1350-4851; 1466-4291
• DOI: 10.1080/13504851.2019.1591590

A number of recent studies in the macro-finance literature that addresses the link between asset prices and economic fluctuations have focused on the usefulness of various factor models in the context of now-casting using very big dataset. The issue of factor extraction is usually swept under the carpet in the factor model literature, where it seems that all that is needed is a large number of economic and financial variables. We contribute to this literature by analysing whether factor estimation methods matters for the performance of factor-based now-casting models based on selected emerging markets GDP. Ancillary findings based on our GDP now-casting experiments on major emerging market countries underscore the advantage of sparse principal component analysis-based factor estimation approach. These results show that imposing a sparse structure on the whole dataset is generally a useful step towards reducing the forecast errors in the context of GDP now-casting model specification.