PUBLICATIONS

Accurate real-time macroeconomic data are essential for policy-making and economic nowcasting. The rising interest in analyses at the sub-national level cannot be served as such data are currently not available. In this paper, I introduce a real-time database for German regional economic accounts. The database contains real-time information for nine macroeconomic aggregates and the 16 German states. I conduct both a revision analysis and a nowcasting experiment for real gross domestic product. By pooling the states together, the first official estimates show no systematic revision errors. The pooling, however, suppresses the revision characteristics of single states. For half of the 16 German states I find that the first estimates are no optimal predictions, thus, leaving room for improvements in the future. The real-time nowcasts for real gross domestic product growth based on a mixed-frequency vector autoregression are very accurate and beat several benchmark models. More regional data would help to better inform the model, thereby increasing its nowcast performance even further.

[Journal] [Working Paper] [Appendix] [Data]

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high-frequency electricity consumption data from industrial firms for the second-largest German state, the Free State of Bavaria, we conduct a pseudo out-of-sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high-frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only two weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity.

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This paper applies component-wise boosting to the topic of regional economic forecasting. Component-wise boosting is a pre-selection algorithm of indicators for forecasting. By using unique quarterly real gross domestic product data for two German states (the Free State of Saxony and Baden-Wuerttemberg) and Eastern Germany for the period from 1997 to 2013, in combination with a large data set of monthly indicators, we show that boosting is generally doing a very good job in regional economic forecasting. We additionally take a closer look into the algorithm and ask which indicators get selected. All in all, boosting outperforms our benchmark model for all the three regions considered. We also find that indicators that mirror the region-specific economy get frequently selected by the algorithm.

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We tackle the nowcasting problem at the regional level, using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly take into account the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each quarter. It appears that regional survey results in particular improve forecasting accuracy. Among the 10% best performing models for the short forecasting horizon, one fourth contain regional indicators. Hard indicators from the German manufacturing sector and the Composite Leading Indicator for Europe also deliver useful information for the prediction of regional GDP in Saxony. Unlike national GDP forecasts, the performance of regional GDP is similar across different information sets within a quarter.

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In this study, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden-Württemberg) and Eastern Germany. We overcome the problem of a ‘data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single-indicator, multi-indicator, pooled and factor forecasts in a ‘pseudo-real-time’ setting. Our results show that we can significantly increase forecast accuracy compared with an autoregressive benchmark model, both for short- and long-term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.

[Journal] [Working Paper] [Appendix]

Over the last decade, the topic of regional economic forecasting has become increasingly prevalent in academic literature. The most striking problem in this context is data availability at a regional level. However, considerable methodological improvements have been made to address this problem. This paper summarises a multitude of articles from academic journals and describes state-of-the-art techniques in regional economic forecasting. After identifying current practices, the article closes with a roadmap for possible future research activities.

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In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral sub-components at the regional level. With an autoregressive distributed lag model we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different regional levels (international, national and regional) and additionally make usage of several forecast pooling strategies and factor models. Our results show that we are able to increase forecast accuracy of GVA for every sector and for all forecast horizons (one up to four quarters) compared to an autoregressive process. Finally, we show that sectoral forecasts contain more information in the short term (one quarter), whereas direct forecasts of total GVA are preferable in the medium (two and three quarters) and long term (four quarters).

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This analysis investigates the predictive power of the headline indices of the four most important German survey providers. We conduct an out-of-sample, real-time forecast experiment for growth of total and private sector gross domestic product and growth of gross value added in both the manufacturing and the service sector. All providers publish valuable leading indicators for both GDP measures, with some advantages for the ifo indicators and the Economic Sentiment Indicator, respectively. For the manufacturing sector, indicators provided by the ifo Institute are clearly superior. For the service sector, all indicators prove to have a similar nowcasting performance, whereas the Economic Sentiment Services of the Centre for European Research is preferable for one quarter-ahead predictions.

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Globalization has led to huge increases in import volumes. Since imports fluctuate heavily over time, they are difficult to forecast and reliable leading indicators are needed. Our paper introduces the first leading indicator to forecast import growth, the Import Climate. While surveys are an often-used source for leading indicators, these data typically do not include information about expected import demand of firms and households. Therefore, our approach builds on the idea that import demand of the domestic country should be reflected in the expected export developments of its main trading partners, which can be measured by standard surveys. We show for six advanced economies that the Import Climate outperforms standard business cycle indicators that mainly reflect domestic demand. Thus, the Import Climate is a reliable tool for import forecasting for both academics and policymakers.

[Journal] [Working Paper] [Appendix] [Data]

In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo-out-of-sample analyses reveal that the best performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best performing indicators and thus increasing the accuracy of export forecasts.

[Journal] [Working Paper] [Appendix]

Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and instantaneous available source of leading indicators, the ifo World Economic Survey (WES), to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for more than three-fourths of the countries or country-aggregates in our sample, a model containing one of the major WES indicators produces on average lower forecast errors compared to a benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And third, adding the WES indicators of the main trading partners leads to a further increase in forecast accuracy in more than 50% of the countries. It seems therefore reasonable to incorporate economic signals from the domestic economy’s main trading partners.

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In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.

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In this paper we evaluate the forecasting performance of employment expectations for employment growth in 15 European states. Our data cover the period from the first quarter 1998 to the fourth quarter 2014. With in-sample analyses and pseudo out-of-sample exercises, we find that for most of the European states considered, the survey-based indicator model outperforms common benchmark models. It is therefore a powerful tool for generating more accurate employment forecasts. We observe the best results for one quarter ahead predictions that are primarily the aim of the survey question. However, employment expectations also work well for longer forecast horizons in some countries.

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This article looks into the ‘fine print’ of boosting for economic forecasting. By using German industrial production for the period from 1996 to 2014 and a data set consisting of 175 monthly indicators, we evaluate which indicators get selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard indicators like turnovers, as well as a small number of survey results, get selected frequently by the algorithm and are therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply that never get chosen by the boosting approach at all.

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To date, only annual information on economic activity is published for the 16 German states, thus, preventing the measurement of inter-annual business cycle movements. We calculate quarterly regional real GDP estimates for the period between 1995 to 2021, thereby improving the database for Germany. We use the new data for an in-depth business cycle analysis including the discussion of regional business cycle characteristics, the factors explaining these features, and the first state-level output gap estimates at the quarterly frequency. The analysis reveals large heterogeneities in both the duration and amplitudes of state-specific fluctuations as well as in the degrees of cyclical concordance. Especially the sectoral mix significantly explains the differences across the states’ business cycle amplitudes. We further observe heterogeneous output gap profiles across the German states that also correlate with the regional sectoral mix. In 2021, the states with negative output gaps were those with a large share in service activities, which is the sector most affected by the Covid-19 pandemic.

[Journal] [Working Paper] [Appendix] [Data]

In this paper, we outline material and capital linkages across sectors to quantify the role of the German production network in amplifying sectoral dynamics on aggregate trend gross domestic product growth. This allows us to study the impact of sectoral labor input and total factor productivity trend growth variation on the persistent decline in long-run output growth. Our estimation reveals that sector-specific developments have historically accounted for half of this long-term decline. Zooming into the reunification period, we find a pronounced decline of total factor productivity growth in Professional and Business Services together with a fall in labor input growth in the Construction sector to drive the sharp decline of German trend output growth over the 1990s. We further document significant changes regarding the sectors’ importance as input suppliers to the economy over the past decades. Our analysis identifies the labor-intensive Construction sector as a major input hub in the production network, its long-run amplification effect exceeding four times its share in value added. Given the impending demographic change, the low potential for automation in this sector may significantly reduce future German trend output growth.

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We study whether technology gains in sectors related to Information and Communications Technology (ICT) increase productivity in the rest of the economy. To separate exogenous gains in ICT from other technological progress, we use the relative price of ICT goods and services in a structural VAR with medium-run restrictions. Using local projections to estimate the effect of ICT-related technology gains on sectoral technology (TFP), we find two sets of results. First, since the mid-2000s there have been positive and persistent technology spillovers to sectors intensively using ICT. Second, neglecting leasing activity leads to an overestimation of the TFP response for all sectors except the leasing sector, where it is strongly underestimated.

[Journal] [Working Paper] [Appendix] [Shocks]

The ifo Institute is Germany’s largest business survey provider, with the ifo Business Climate Germany as one of the most important leading indicators for gross domestic product. However, the ifo Business Survey is not solely limited to the Business Climate and also delivers a multitude of further indicators to forecast several important economic variables. This paper gives a literature overview over existing studies that deal with the forecasting power of various ifo indicators both for gross domestic product and further economic variables such as exports. Overall, the various indicators from the ifo Business Survey can be seen as leading indicators for a multitude of variables representing the German economy, making them a powerful tool both for an in-depth business cycle diagnosis and for applied forecasting work.

[Journal] [Working Paper] [Appendix]

In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo-out-of-sample analyses reveal that the best performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best performing indicators and thus increasing the accuracy of export forecasts.

[Journal] [Working Paper]

We estimate potential regional industrial effects in case of a threatening gas deficit. For Germany, the reduction leads to a potential decrease in industrial value added by 1.6%. The heterogeneity across German states is remarkable, ranging from 2.2% for Rhineland-Palatinate to 0.7% for Hamburg. We emphasize the need for regional input-output tables to conduct economic analysis on a sub-national level, particularly when regional industrial structures are heterogeneous. The approximation with national figures can lead to results that differ both in magnitude and relative regional exposure. Our findings highlight that more accurate policy guidance can be achieved by improving the regional database.

[Journal] [Working Paper] [Appendix]

In a globalized world with high international factor mobility, crises often spread quickly over large parts of the world. Politicians carry a vital interest in keeping crises as small and short as possible. Against this background we study whether the type of company of owner-managed SMEs, in Germany well-known as Mittelstand firms, helps increasing an economy’s crisis resistance. We study this issue at the example of the Great Recession of the years 2008/2009. Using micro panel data from the ifo Business Survey, we study the comparative performance of Mittelstand enterprises and find supporting evidence for the hypothesis that Mittelstand firms performed more stable throughout the Great Recession than non-Mittelstand firms. We also show that owner-managed SMEs performed significantly better than SMEs and owner-managed large enterprises. Thus, it is rather the combination of firm size and owner-management that leads to more crisis resistance.

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Media Coverage: Deutscher Mittelstands-Bund (DMB)

The performance of parties at the national level is likely to influence election results at the local level, and vice versa. However, researchers have not yet quantified those electoral externalities. We apply vector autoregressions with predetermined variables to new high-frequency opinion poll data for the German state of Berlin, measuring voting intentions of Berlin voters for the state parliament and for the national parliament. Our results show that the impact of local politics on national elections has been drastically underestimated so far. Shocks in state parliament voting intentions influence national parliament voting intentions to the very same extent as vice versa. Externalities account for around 10% to 40% of the variation at the other level of government. Left-wing parties interact somewhat more strongly between different levels of government than right-wing parties, and effects are more persistent.

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This paper investigates the main determinants of the representation of foreign employees across German regions. Since migration determinants are not necessarily the same for workers of different nationalities, spatial patterns are explained not only for total foreign employment but also for the 35 most important migration countries to Germany. Based on a total census for all 402 German districts, the paper starts by showing the spatial distributions of workers with different nationalities and explains the emerging patterns by spatial error models. Although large heterogeneity in determinants across nationalities are found, similarities between country groups prevail. Economic conditions matter for most nationalities, whereas the importance of amenities and openness differ.

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Sustainable budgets are important quality signals for the electorate. Politicians might thus have an incentive to influence tax revenue forecasts, which are widely regarded as a key element of national budget plans. Looking at the time period from 1996 to 2012, we systematically analyze whether national tax revenue forecasts in 18 OECD countries are biased due to political manipulation. Drawing on theories from the field of political economy, we test three hypotheses using panel estimation techniques. We find support for partisan politics. Left-wing governments seem to produce more optimistic, or less pessimistic, tax revenue forecasts than do right-wing ones. Contrary to the theoretical prediction based on the “common pool” problem, we find that more fragmented governments and parliaments tend to produce more pessimistic, or less optimistic, tax revenue forecasts. We find no empirical evidence that political business cycles play a role in tax revenue forecasts.

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The debate on localization and urbanization economies usually neglects interdependencies between the two types of economies. This paper addresses this problem by employing an interaction model using German data covering four different sectors for the years 1998 to 2008. We find that localization and urbanization economies interact negatively in most of the sectors. Furthermore, we study non-linear effects of specialization and diversification. Taking these into account, the signs indicating localization and urbanization economies change at certain thresholds. Our regression analysis shows evidence of localization economies in manufacturing and basic services. Urbanization economies are generally less prevalent in our data.

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Rankings for sports such as chess or table tennis are based on the so called Elo rating system. In this paper, we apply this rating system to rank economics journals. One main advantage of the Elo ranking compared to existing ones is its explicit consideration of a journal's performance path. Another advantage is the easy application of the system to any journal metric that is published on a regular basis. Our application is based on data from Web of Science that comprises the impact factors of 382 economics journals for the period from 1997 to 2016. The most recent Elo ranking is quite different for rather 'middle-class' journals compared to other existing rankings. However, also some differences for the top 30 emerge.

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In this paper we transfer the Elo rating system, which is widely accepted in chess, sports and other disciplines, to rank scientific journals. The advantage of the Elo system is the explicit consideration of the factor time and the history of a journal's ranking performance. Most other rankings that are commonly applied neglect this fact. The Elo ranking methodology can easily be applied to any metric, published on a regular basis, to rank journals. We illustrate the approach using the SNIP indicator based on citation data from Scopus. Our data set consists of more than 20 000 journals from many scientific fields for the period from 1999 to 2015. We show that the Elo approach produces similar but by no means identical rankings compared to other rankings based on the SNIP alone or the Tournament Method. Especially the rank order for rather ‘middle-class’ journals can tremendously change.

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