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Comparison of Simple Sum and Divisia Monetary Aggregates in GDP Forecasting: A Support Vector Machines Approach

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  • Periklis Gogas

    ()
    (Department of International Economic Relations and Development, Democritus University of Thrace, Greece)

  • Theophilos Papadimitriou

    (Department of International Economic Relations and Development, Democritus University of Thrace, Greece)

  • Elvira Takli

    (Department of International Economic Relations and Development, Democritus University of Thrace, Greece)

Abstract

In this study we compare the forecasting ability of the simple sum and Divisia monetary aggregates with respect to U.S. gross domestic product. We use two alternative Divisia aggregates, the series produced by the Center for Financial Stability (CFS Divisia) and the ones produced by the Federal Reserve Bank of St. Louis (MSI Divisia). The empirical analysis is done within a machine learning framework employing a Support Vector Regression (SVR) model equipped with two kernels: the linear and the radial basis function kernel. Our training data span the period from 1967Q1 to 2007Q4 and the out-of-sample forecasts are performed on a one quarter ahead forecasting horizon on the period 2008Q1 to 2011Q4. Our tests show that the Divisia monetary aggregates are superior to the simple sum monetary aggregates in terms of standard forecast evaluation statistics.

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 04_13.

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Date of creation: Jan 2013
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Handle: RePEc:rim:rimwps:04_13

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Keywords: GDP forecasting; SVR; Simple Sum; Divisia;

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Cited by:
  1. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
  2. Ioannis Praggidis & Periklis Gogas & Vasilios Plakandaras & Theophilos Papadimitriou, 2013. "Fiscal shocks and asymmetric effects: a comparative analysis," Papers 1312.2693, arXiv.org.

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