A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle
Ginters Buss ()
MPRA Paper from University Library of Munich, Germany
We build a small-scale factor model for the GDP of one of the hardest hit economies during the latest recession to study the exact dynamic versus static factor model performance along a business cycle, with an emphasis placing on nowcasting performance during a pronounced switch of business cycle phases due to the latest recession. We compare the factor models' nowcasting performance to a random walk, autoregressive and the best-performing nowcasting models at our hands, which are vector autoregressive (VAR) models. It is shown that a small-scale static factor-augmented VAR (FAVAR) model tends to improve upon the nowcasting performance of the VAR models when the model span and the nowcasting period stretch beyond a single business cycle phase, while exact dynamic factor models tend to fail to detect the timing and depth of the recession regardless of ARMA specifications. As regards the case when the model span and the nowcasting period are contained within a single business cycle phase, static and dynamic factor models appear to show similar performance with potentially slight superiority of dynamic factor models if the factor-forming set of variables and factor dynamics are carefully selected.
Keywords: nowcasting; business cycle; static versus dynamic factors; small-scale FAVAR; VAR; GDP (search for similar items in EconPapers)
JEL-codes: C32 C53 C52 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-bec, nep-fdg and nep-for
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