Nowcasting Finnish real economic activity: a machine learning approach
Paolo Fornaro () and
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Henri Luomaranta: Research Institute of the Finnish Economy
Empirical Economics, 2020, vol. 58, issue 1, No 4, 55-71
Abstract We develop a nowcasting framework, based on microlevel data, to provide faster estimates of the Finnish monthly real economic activity indicator, the Trend Indicator of Output (TIO), and of quarterly GDP. We use firm-level turnovers, which are available shortly after the end of the reference month, and real-time traffic volumes data, to form our set of predictors. We rely on combinations of nowcasts obtained from a range of statistical models and machine learning techniques which are able to handle high-dimensional information sets. The results of our pseudo-real-time analysis indicate that a simple nowcast combination based on these models provides faster estimates of TIO and GDP, without increasing substantially the revision error.
Keywords: Flash estimates; Machine learning; Microlevel data; Nowcasting (search for similar items in EconPapers)
JEL-codes: C33 C55 E37 (search for similar items in EconPapers)
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