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
Externe Publikationen,
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
Die Schulden- und Vertrauenskrise in Europa hat eine intensive Diskussion über die makroökonomische Koordinierung ausgelöst. Die bestehenden Institutionen, darunter auch der Stabilitäts- und Wachstumspakt, haben sich als Krisenpräventions- und Krisenmanagementinstrumente nicht bewährt. Ein Vorschlag in der gegenwärtigen Debatte lautet, anhand geeigneter Frühindikatoren eine regelmäßige und systematische makroökonomische
Überwachung vorzunehmen, um sich anbahnende Krisen früh erkennen und darauf reagieren zu können. Dieser Beitrag stellt die Prognosegüte von vier vorgeschlagenen Indikatorensets vergleichend dar, wobei sowohl die Güte
von Einzelindikatoren als auch die Güte aggregierter Gesamtindikatoren betrachtet werden. Die verschiedenen Einzelindikatoren weisen eine sehr unterschiedliche Prognosequalität auf, wobei sich neben dem Staatsdefizit
besonders die Arbeitsmarktindikatoren, die private Verschuldung und der Leistungsbilanzsaldo durch eine hohe Prognosegüte auszeichnen. Unter den Gesamtindikatoren schneiden besonders jene gut ab, die sowohl viele unterschiedliche als auch besonders gute Einzelindikatoren beinhalten. Deshalb wird für den Einsatz eines breit basierten Gesamtindikators bei der makroökonomischen Überwachung plädiert. Dieser sollte zudem aus gleichgewichteten Einzelindikatoren zusammengesetzt sein, um der Tatsache Rechnung zu tragen, dass die Ursachen künftiger Krisen vorab nicht bekannt sind.
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Macroeconomic Imbalances as Indicators for Debt Crises in Europe
Tobias Knedlik, Gregor von Schweinitz
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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The Financial Crisis from a Forecaster’s Perspective
Katja Drechsel, Rolf Scheufele
Abstract
This paper analyses the recession in 2008/2009 in Germany, which is very different from previous recessions, in particular regarding its cause and magnitude. We show to what extent forecasters and forecasts based on leading indicators fail to detect the timing and the magnitude of the recession. This study shows that large forecast errors for both expert forecasts and forecasts based on leading indicators resulted during this recession which implies that the recession was very difficult to forecast. However, some leading indicators (survey data, risk spreads, stock prices) have indicated an economic downturn and hence, beat univariate time series models. Although the combination of individual forecasts provides an improvement compared to the benchmark model, the combined forecasts are worse than several individual models. A comparison of expert forecasts with the best forecasts based on leading indicators shows only minor deviations. Overall, the range for an improvement of expert forecasts during the crisis compared to indicator forecasts is relatively small.
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