Reassessing EU Comparative Advantage: The Role of Technology
Filippo di Mauro, Marco Matani, Gianmarco Ottaviano
IWH-CompNet Discussion Papers,
Nr. 2,
2024
Abstract
Based on the sufficient statistics approach developed by Huang and Ottaviano (2024), we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation.
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Reassessing EU Comparative Advantage: The Role of Technology
Filippo di Mauro, Marco Matani, Gianmarco Ottaviano
IWH Discussion Papers,
Nr. 26,
2024
Abstract
Based on the sufficient statistics approach developed by Huang and Ottaviano (2024), we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation.
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
Nr. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Tracking Weekly State-Level Economic Conditions
Christiane Baumeister, Danilo Leiva-León, Eric Sims
Review of Economics and Statistics,
Nr. 2,
2024
Abstract
This paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.
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08.02.2024 • 3/2024
IWH-Insolvenztrend: Zahl der Firmenpleiten weiterhin hoch – Corona-Hilfen für schwache Unternehmen sind ein Grund
Nach dem Rekordwert im Dezember bleibt die Zahl der Insolvenzen von Personen- und Kapitalgesellschaften im Januar auf unverändert hohem Niveau, zeigt die aktuelle Analyse des Leibniz-Instituts für Wirtschaftsforschung Halle (IWH). Erklären lässt sich die heutige Lage auch mit den Staatshilfen während der Corona-Pandemie.
Steffen Müller
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Medienecho
Medienecho Dezember 2024 Oliver Holtemöller: So teuer sind die Wahlversprechen der Parteien in: Handelsblatt, 19.12.2024 IWH: Experten: Deutsche Wirtschaft schrumpft 2024 doch…
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Alumni
IWH-Alumni Das IWH pflegt den Kontakt zu seinen ehemaligen Mitarbeiterinnen und Mitarbeitern weltweit. Wir beziehen unsere Alumni in unsere Arbeit ein und unterrichten diese…
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Herding Behavior and Systemic Risk in Global Stock Markets
Iftekhar Hasan, Radu Tunaru, Davide Vioto
Journal of Empirical Finance,
September
2023
Abstract
This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.
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A Comparison of Monthly Global Indicators for Forecasting Growth
Christiane Baumeister, Pierre Guérin
International Journal of Forecasting,
Nr. 3,
2021
Abstract
This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.
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Alone at Home: The Impact of Social Distancing on Norm-consistent Behavior
Sabrina Jeworrek, Joschka Waibel
IWH Discussion Papers,
Nr. 8,
2021
Abstract
Around the globe, the COVID-19 pandemic has turned daily live upside down since social distancing is probably the most effective means of containing the virus until herd immunity is reached. Social norms have been shown to be an important determinant of social distancing behaviors. By conducting two experiments and using the priming method to manipulate social isolation recollections, we study whether social distancing has in turn affected norms of prosociality and norm compliance. The normative expectations of what behaviors others would approve or disapprove in our experimental setting did not change. Looking at actual behavior, however, we find that persistent social distancing indeed caused a decline in prosociality – even after the relaxation of social distancing rules and in times of optimism. At the same time, our results contain some good news since subjects seem still to care for norms and become more prosocial once again after we draw their attention to the empirical norm of how others have previously behaved in a similar situation.
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