Nowcasting Swedish GDP with a large and unbalanced data set

Ard Reijer () and Andreas Johansson ()
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Ard Reijer: Sveriges Riksbank
Andreas Johansson: Stockholm School of Economics

Empirical Economics, 2019, vol. 57, issue 4, No 11, 1373 pages

Abstract: Abstract We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance relative to univariate benchmarks improves.

Keywords: Nowcast; MIDAS; Factor models; Pooling (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00181-018-1500-1

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