Using newspapers for tracking the business cycle: a comparative study for Germany and Switzerland

Abstract

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 On the basis of keyword searches in newspaper articles several versions of the Recession-word Index (RWI) are constructed for Germany and Switzerland. We use these indices in order to track the business cycle dynamics in these two countries. Our main findings are the following. First, we show that augmenting benchmark autoregressive models with the RWI generally leads to improvement in accuracy of one-step ahead forecasts of GDP growth compared to those obtained by the benchmark model. Second, the accuracy of out-of-sample forecasts obtained with models augmented with the RWI is comparable to that of models augmented with established economic indicators in both countries, such as the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment for Germany, and the KOF Economic Barometer and the Purchasing Managers Index in manufacturing for Switzerland. Third, we show that the RWI-based forecasts are more accurate than the consensus forecasts (published by Consensus Economics Inc.) for Switzerland, whereas we reach the opposite conclusion for Germany. In fact, the accuracy of the consensus forecasts of GDP growth for Germany appears to be superior to that of any other indicator considered in our study. These results are robust to changes in estimation/forecast samples, the use of rolling vs expanding estimation windows, and the inclusion of a web-based recession indicator extracted from Google Trends into a set of the competing models. Terms of use: Documents in

Do Media Data Help to Predict German Industrial Production?

“…Grossarth‐Maticek and Mayr () find that the R‐word index is suitable for detecting turning points in the business cycle in real time. Iselin and Siliverstovs () find that media indicators are helpful for one‐quarter‐ahead forecasts. Partly, they are even better than other indicators.…”

“…Second, both studies consider only short‐term horizons. Grossarth‐Maticek and Mayr () focus on the first two lags (1 and 2 quarters), whereas Iselin and Siliverstovs () concentrate only on the one‐step‐ahead forecast. In contrast, we examine different horizons varying between 1 and 12 months ahead.…”

“…The results are rather mixed for both indices. Other media studies include Iselin and Siliverstovs (), who use the R‐word index to make one‐step‐ahead forecasts of the growth rates of real GDP in Germany and Switzerland. While media indicators are helpful for Switzerland, it is not the case for Germany.…”

Enforcement, Managerial Discretion, and the Informativeness of Accruals

“…For example, many studies that evaluate the effects of the SOX in the United States have struggled to find appropriate control samples (Leuz & Wysocki, 2016). This study exploits that two neighboring countries, Austria and Switzerland, are comparable with Germany along important dimensions such as institutional characteristics, accounting traditions, and macroeconomic conditions (Daske & Gebhardt, 2006;IMF, 2013;Iselin & Siliverstovs, 2016;Kaufmann et al, 2009;Spamann, 2010) but had no significant enforcement changes during the study's sample period (Brown et al, 2014;Christensen et al, 2013). Thus, publicly listed firms in Austria and Switzerland provide a useful control sample for a difference-in-differences (DiD) analysis.…”

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

“…Our findings are in line with recent research by [53], who found that the use of optimized news-based sentiment values yielded accuracy gains for forecasting US industrial production. For Switzerland and Germany, [54] obtained improvements in accuracy of one-step-ahead GDP forecasts by augmenting benchmark autoregressive models with variations in the recession-word index. Similarly, [55] found that accounting for consumer and business sentiments led to the improved forecast accuracy of consumption in Indonesia.…”