Mittelfristprojektion des IWH: Wirtschaftsentwicklung und Öffentliche Finanzen 2018 bis 2025
Andrej Drygalla, Katja Heinisch, Oliver Holtemöller, Axel Lindner, Matthias Wieschemeyer, Götz Zeddies
Konjunktur aktuell,
No. 4,
2018
Abstract
In Deutschland wird die Anzahl der Erwerbspersonen mittelfristig aufgrund der Alterung der Bevölkerung sinken und damit auch das Wirtschaftswachstum niedriger ausfallen als in den vergangenen Jahren. Gleichzeitig hat die Bundesregierung eine Reihe von zusätzlichen Staatsausgaben beschlossen. Auf der Grundlage einer gesamtwirtschaftlichen Projektion mit dem IWH-Deutschlandmodell lässt sich aber zeigen, dass es bis zum Jahr 2025 kaum zu Haushaltsdefiziten kommt, auch wenn sämtliche im Koalitionsvertrag enthaltenen finanzpolitischen Maßnahmen umgesetzt werden. Selbst wenn sich die makroökonomischen Rahmenbedingungen verschlechtern, etwa wegen eines deutlichen Zinsanstiegs oder eines Einbruchs der ausländischen Nachfrage, würde der Finanzierungssaldo zwar negativ, die zu erwartenden Defizite lägen aber dennoch wohl unter 0,5% in Relation zum Bruttoinlandspro-dukt. Ein Einbruch der ausländischen Nachfrage würde die Produktion zwar stärker dämpfen als ein Zinsschock, die Effekte auf den gesamtstaatlichen Finanzierungssaldo wären aber vergleichbar. Denn ein Zinsschock belastet eher die Binnennachfrage, von deren Rückgang die staatlichen Einnahmen stärker betroffen sind als von einem Rückgang der Exporte. Für die kommenden Jahre dürfte der deutsche Staatshaushalt damit recht robust sein; dabei ist aber zu beachten, dass etwa die aus dem Rentenpaket resultierenden Mehrausgaben erst nach dem Jahr 2025 deutlich zu Buche schlagen.
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Expectation Formation, Financial Frictions, and Forecasting Performance of Dynamic Stochastic General Equilibrium Models
Oliver Holtemöller, Christoph Schult
Abstract
In this paper, we document the forecasting performance of estimated basic dynamic stochastic general equilibrium (DSGE) models and compare this to extended versions which consider alternative expectation formation assumptions and financial frictions. We also show how standard model features, such as price and wage rigidities, contribute to forecasting performance. It turns out that neither alternative expectation formation behaviour nor financial frictions can systematically increase the forecasting performance of basic DSGE models. Financial frictions improve forecasts only during periods of financial crises. However, traditional price and wage rigidities systematically help to increase the forecasting performance.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Heinisch, Rolf Scheufele
Empirical Economics,
No. 2,
2018
Abstract
In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.
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Predicting Earnings and Cash Flows: The Information Content of Losses and Tax Loss Carryforwards
Sandra Dreher, Sebastian Eichfelder, Felix Noth
Abstract
We analyse the relevance of losses, accounting information on tax loss carryforwards, and deferred taxes for the prediction of earnings and cash flows up to four years ahead. We use a unique hand-collected panel of German listed firms encompassing detailed information on tax loss carryforwards and deferred taxes from the tax footnote. Our out-of-sample predictions show that considering accounting information on tax loss carryforwards and deferred taxes does not enhance the accuracy of performance forecasts and can even worsen performance predictions. We find that common forecasting approaches that treat positive and negative performances equally or that use a dummy variable for negative performance can lead to biased performance forecasts, and we provide a simple empirical specification to account for that issue.
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Bank Overall Financial Strength: Islamic Versus Conventional Banks
Michael Doumpos, Iftekhar Hasan, Fotios Pasiouras
Economic Modelling,
2017
Abstract
A number of recent studies compare the performance of Islamic and conventional banks with the use of individual financial ratios or efficiency frontier techniques. The present study extends this strand of the literature, by comparing Islamic banks, conventional banks, and banks with an Islamic window with the use of a bank overall financial strength index. This index is developed with a multicriteria methodology that allows us to aggregate various criteria capturing bank capital strength, asset quality, earnings, liquidity, and management quality in controlling expenses. We find that banks differ significantly in terms of individual financial ratios; however, the difference of the overall financial strength between Islamic and conventional banks is not statistically significant. This finding is confirmed with both univariate comparisons and in multivariate regression estimations. When we look at the bank financial strength within regions, we find that conventional banks outperform both the Islamic banks and the banks with Islamic window in the case of Asia and the Gulf Cooperation Council; however, Islamic banks perform better in the MENA and Senegal region. Second stage regressions also reveal that the bank overall financial strength index is influenced by various country-specific attributes. These include control of corruption, government effectiveness, and operation in one of the seven countries that are expected to drive the next big wave in Islamic finance.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
Abstract
In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Die mittelfristige wirtschaftliche Entwicklung in Deutschland für die Jahre 2016 bis 2021
Hans-Ulrich Brautzsch, Katja Heinisch, Oliver Holtemöller, Brigitte Loose, Matthias Wieschemeyer, Götz Zeddies
Konjunktur aktuell,
No. 4,
2016
Abstract
Nach der Mittelfristprojektion des IWH dürfte das Bruttoinlandsprodukt in Deutschland in den Jahren von 2016 bis 2021 um durchschnittlich 1½% wachsen; das nominale Bruttoinlandsprodukt wird wohl um durchschnittlich 3% zunehmen. Nach einer leichten Überauslastung der Kapazitäten in den Jahren 2016 und 2017 dürfte sich die Produktionslücke mittelfristig schließen. Aufgrund des mittelfristig kaum anziehenden Wachstums im Euroraum und des im Vergleich zum langfristigen Mittel schwachen Welthandels dürften vom Außenhandel in der mittleren Frist kaum Impulse ausgehen; die konjunkturelle Dynamik wird daher nach wie vor maßgeblich von der Inlands¬nachfrage bestimmt. Die Verbraucherpreise ziehen im Prognosezeitraum etwas an.
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Die mittelfristige wirtschaftliche Entwicklung in Deutschland für die Jahre 2015 bis 2020
Hans-Ulrich Brautzsch, Katja Heinisch, Oliver Holtemöller, Brigitte Loose, Götz Zeddies
Konjunktur aktuell,
No. 5,
2015
Abstract
Nach der Mittelfristprojektion des IWH dürfte das Bruttoinlandsprodukt in Deutschland von 2015 bis 2020 um durchschnittlich 1½% wachsen; das nominale Bruttoinlandsprodukt wird wohl um durchschnittlich 3% zunehmen. Nach einer Unterauslastung der Kapazitäten in den Jahren 2015 und 2016 dürfte sich die Produktionslücke im Jahr 2017 schließen. Aufgrund der Erholung des Euroraums und der Weltwirtschaft dürften vom Außenhandel in der mittleren Frist wieder leichte Impulse ausgehen; die konjunkturelle Dynamik wird aber nach wie vor von der Inlandsnachfrage bestimmt. Die Verbraucherpreise ziehen im Prognosezeitraum leicht an.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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