Nowcasting short-term indicators with machine learning methods

Abstract

The monthly European Union (EU) harmonized Short-term Business Statistics (STS) represents one of the most important sources for the assessment of the European economy. Timeliness of STS is of fundamental importance for policy makers to be able to react adequately to sudden economic changes. In the past time lags between reference periods and release dates have been quite considerable. However, European countries selected various approaches to shorten release times like optimizing the short-term statistics sample or increasing efforts to access and integrate administrative data. In this paper different machine learning algorithms for early estimation of missing survey data are evaluated in order to further improve timeliness of Austrian STS data and to increase granularity of early estimates as well. Currently a multivariate time series model is used for early estimation of economic indexes for the highly aggregated level of Total Industry and Construction. This model could be adapted to the level of NACE-Divisions with the exception of a few Divisions with small populations. The quality of the results could be improved for several NACE-Divisions and variables with machine learning methods. However, for the prediction of a few branches with small populations alternative methods have to be developed.