Predicting Finnish economic activity using firm-level data

Paolo Fornaro

International Journal of Forecasting, 2016, vol. 32, issue 1, 10-19

Abstract: In this paper, we compute flash estimates of Finnish monthly economic activity using firm-level data. We use a two-step procedure where the common factors extracted from the firm-level data are subsequently used as predictors in nowcasting regressions. The results show that large firm-level datasets are useful for predicting aggregate economic activity in a timely fashion. The proposed factor-based nowcasting model leads to a superior out-of-sample nowcasting performance relative to the benchmark autoregressive model, even for early nowcasts. Moreover, we find that the quarterly GDP flash estimates that we construct provide a useful real-time alternative to the current official estimates, without any substantial loss of nowcasting accuracy.

Keywords: Firm-level data; Forecasting; Factor model; Real-time data; Large datasets (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:1:p:10-19

DOI: 10.1016/j.ijforecast.2015.04.002

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