Comparison of Simple Sum and Divisia Monetary Aggregates in GDP Forecasting: A Support Vector Machines Approach
Periklis Gogas (),
Theophilos Papadimitriou () and
Elvira Takli ()
Working Paper series from Rimini Centre for Economic Analysis
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.
Keywords: GDP forecasting; SVR; Simple Sum; Divisia (search for similar items in EconPapers)
JEL-codes: C22 E47 E50 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mon
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Journal Article: Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:04_13
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