Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Empirical Economics,
No. 1,
2020
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Sinkendes Potenzialwachstum in Deutschland, beschleunigter Braunkohleausstieg und Klimapaket: Finanzpolitische Konsequenzen für die Jahre bis 2024
Andrej Drygalla, Katja Heinisch, Oliver Holtemöller, Axel Lindner, Christoph Schult, Matthias Wieschemeyer, Götz Zeddies
Konjunktur aktuell,
No. 4,
2019
Abstract
Nach der Mittelfristprojektion des IWH wird das Bruttoinlandsprodukt in Deutschland in den Jahren bis 2024 preisbereinigt um durchschnittlich 1% wachsen; das nominale Bruttoinlandsprodukt wird um durchschnittlich 2¾% zunehmen. Die Durchschnittswerte verschleiern die Tatsache, dass das Wachstum gegen Ende des Projektionszeitraums aufgrund der dann rückläufigen Erwerbsbevölkerung spürbar zurückgehen wird. Dies wird sich auch bei den Staatseinnahmen niederschlagen. Allerdings wird die Bevölkerung nicht regional gleichverteilt zurückgehen. Strukturschwache Regionen dürften stärker betroffen sein. Die regionalen Effekte auf die Staatseinnahmen werden zwar durch Umverteilungsmechanismen abgefedert, aber nicht völlig ausgeglichen. Regionen mit schrumpfender Erwerbsbevölkerung müssen sich auf einen sinkenden finanziellen Spielraum einstellen. Der beschleunigte Braunkohleausstieg wird diesen Prozess verstärken, das Klimapaket der Bundesregierung hat hingegen vergleichsweise geringe Auswirkungen auf die öffentlichen Finanzen.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
No. 4,
2019
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 surveys and financial 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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How Forecast Accuracy Depends on Conditioning Assumptions
Carola Engelke, Katja Heinisch, Christoph Schult
IWH Discussion Papers,
No. 18,
2019
Abstract
This paper examines the extent to which errors in economic forecasts are driven by initial assumptions that prove to be incorrect ex post. Therefore, we construct a new data set comprising an unbalanced panel of annual forecasts from different institutions forecasting German GDP and the underlying assumptions. We explicitly control for different forecast horizons to proxy the information available at the release date. Over 75% of squared errors of the GDP forecast comove with the squared errors in their underlying assumptions. The root mean squared forecast error for GDP in our regression sample of 1.52% could be reduced to 1.13% by setting all assumption errors to zero. This implies that the accuracy of the assumptions is of great importance and that forecasters should reveal the framework of their assumptions in order to obtain useful policy recommendations based on economic forecasts.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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For How Long Do IMF Forecasts of World Economic Growth Stay Up-to-date?
Katja Heinisch, Axel Lindner
Applied Economics Letters,
No. 3,
2019
Abstract
This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.
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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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