Outperforming IMF Forecasts by the Use of Leading Indicators
Katja Drechsel, Sebastian Giesen, Axel Lindner
IWH Discussion Papers,
No. 4,
2014
Abstract
This study analyzes the performance of the IMF World Economic Outlook forecasts for world output and the aggregates of both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current and the next year, we examine whether IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time dataset for GDP and for the 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 if the publication of the Outlook is only a few months old.
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Modelling Macroeconomic Risk: The Genesis of the European Debt Crisis
Gregor von Schweinitz
Hochschulschrift, Online-Publikation,
2013
Abstract
Diverging European sovereign bond yields after 2008 are the most visible sign of the European debt crisis. This dissertation examines in a first step, to which extent the development of yields is driven by credit and liquidity risk, and how it is influenced by general uncertainty on financial markets. It can be shown that yields are driven to a significant degree by a flight towards bonds of high liquidity in times of high market uncertainty. In a second step, high yields are interpreted as a sign of an existing crisis in the respective country. Using the signals approach, the early-warning capabilities of four different proposals for the design of the scoreboard as part of the “Macroeconomic Imbalances Procedure” (introduced in December 2011 by the European Commission) are tested, advocating a scoreboard including a variety of many different indicators. In a third step, the methodology of the signals approach is extended to include also results on significance.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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The European Commission’s Scoreboard of Macroeconomic Imbalances – The impact of preferences on an early warning system
Tobias Knedlik
External Publications,
2013
Abstract
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Predicting Financial Crises: The (Statistical) Significance of the Signals Approach
Makram El-Shagi, Tobias Knedlik, Gregor von Schweinitz
Journal of International Money and Finance,
No. 35,
2013
Abstract
The signals approach as an early-warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it cannot distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful results; (2) that composite indicators aggregating information contained in individual indicators add value to the signals approach; and (3) that indicators which are found to be significant in-sample usually perform similarly well out-of-sample.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay.
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Macroeconomic Imbalances as Indicators for Debt Crises in Europe
Tobias Knedlik, Gregor von Schweinitz
Journal of Common Market Studies,
No. 5,
2012
Abstract
European authorities and scholars published proposals on which indicators of macroeconomic imbalances might be used to uncover risks for the sustainability of public debt in the European Union. We test the ability of four proposed sets of indicators to send early-warnings of debt crises using a signals approach for the study of indicators and the construction of composite indicators. We find that a broad composite indicator has the highest predictive power. This fact still holds true if equal weights are used for the construction of the composite indicator in order to reflect the uncertainty about the origin of future crises.
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Predicting Financial Crises: The (Statistical) Significance of the Signals Approach
Makram El-Shagi, Tobias Knedlik, Gregor von Schweinitz
Abstract
The signals approach as an early warning system has been fairly successful in detecting crises, but it has so far failed to gain popularity in the scientific community because it does not distinguish between randomly achieved in-sample fit and true predictive power. To overcome this obstacle, we test the null hypothesis of no correlation between indicators and crisis probability in three applications of the signals approach to different crisis types. To that end, we propose bootstraps specifically tailored to the characteristics of the respective datasets. We find (1) that previous applications of the signals approach yield economically meaningful and statistically significant results and (2) that composite
indicators aggregating information contained in individual indicators add value to the signals approach, even where most individual indicators are not statistically significant on their own.
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Macroeconomic Imbalances as Indicators for Debt Crises in Europe
Tobias Knedlik, Gregor von Schweinitz
Wirtschaft im Wandel,
No. 10,
2011
Abstract
European authorities and scholars published proposals on which indicators of macroeconomic imbalances might be used to uncover risks for the sustainability of public debt in the European Union. We test the ability of four proposed sets of indicators to send early-warnings of debt crises using a signals approach for the study of indicators and the construction of composite indicators. We find that a broad composite indicator has the highest predictive power. This fact still holds true if equal weights are used for the construction of the composite indicator in order to reflect the uncertainty about the origin of future crises.
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