We propose a nowcasting approach for indicators assigned to the Sustainable Development Goal (SDG) 8, calling for decent work and economic growth. The nowcasts of SDG indicators are based on dynamic factor models. In this mixed frequency framework, we exploit information from a comprehensive set of quarterly data to nowcast annually observed SDG indicators. For the model selection and specification search we evaluate the nowcast properties of the models based on a pseudo real-time data set. More recent information on SDGs can disclose a possible deviation from the desired path at an early stage. As an example, we present nowcasts for SDG objectives in Austria for the year 2020. The design of our assessment follows the method and quantitative rules suggested by Eurostat. SDG 8 indicators are highly related to the underlying economic situation and the effects of the COVID-19 pandemic are clearly visible in the results for 2020.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper analyzes the change in the Austrian business cycle over time using data back to 1954. The change in the cyclical pattern is captured using a nonlinear univariate structural time series model where the time of the break point is estimated. Results for GDP series suggest a break in the frequency of the cycle and in the parameter covering the variance of the disturbances of the cycle taking place in the mid 70s and early 80s, respectively. Using data for GDP components a break in these variables is found too, but the timing of the break differs among the series. In a further step the paper assesses the relevance of these findings for forecasting purposes. It is shown that during certain periods the out-of-sample forecasting performance of GDP does improve when a break in one of the two parameters is explicitly modeled. Terms of use: Documents in