Nowcasting and Forecasting GDP in Emerging Markets Using Global Financial and Macroeconomic Diffusion Indexes

33 Pages Posted: 26 Jul 2018

See all articles by Oguzhan Cepni

Oguzhan Cepni

Government of the Republic of Turkey - Central Bank of the Republic of Turkey

Ibrahim Guney

Government of the Republic of Turkey - Central Bank of the Republic of Turkey

Norman R. Swanson

Rutgers University - Department of Economics; Rutgers, The State University of New Jersey - Department of Economics

Date Written: July 5, 2018

Abstract

In this paper, we contribute to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension reduction, machine learning and shrinkage methods including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension reduction method called the Bloomberg Relevance Index (BBG), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. In our empirical analysis, we show that DFMs, when specified using dimension reduction methods (particularly BBG and SPCA), yield superior predictions, relative to benchmark linear econometric or simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies (including Brazil, Mexico, South Africa, and Turkey) that we study. These findings point to the importance of spillover effects across emerging market economies, and underscore the importance of parsimoniously characterizing such linkages when utilizing high dimensional global datasets.

Keywords: diffusion index, dimension reduction methods, emerging markets, factor model, forecasting, variable selection

JEL Classification: C53, G17

Suggested Citation

Cepni, Oguzhan and Guney, Ibrahim and Swanson, Norman Rasmus and Swanson, Norman Rasmus, Nowcasting and Forecasting GDP in Emerging Markets Using Global Financial and Macroeconomic Diffusion Indexes (July 5, 2018). Available at SSRN: https://ssrn.com/abstract=3208812 or http://dx.doi.org/10.2139/ssrn.3208812

Oguzhan Cepni

Government of the Republic of Turkey - Central Bank of the Republic of Turkey ( email )

Istiklal Cad. 10 Ulus
06100 Ankara, Ankara 06050
Turkey

Ibrahim Guney

Government of the Republic of Turkey - Central Bank of the Republic of Turkey ( email )

Istiklal Cad. 10 Ulus
06100 Ankara, Ankara 06050
Turkey

Norman Rasmus Swanson (Contact Author)

Rutgers, The State University of New Jersey - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States
848-932-7432 (Phone)

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

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