Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
Nr. 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,
Nr. 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,
Nr. 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,
Nr. 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,
Nr. 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
IWH Discussion Papers,
Nr. 30,
2017
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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