2nd FINPRO - Finance and Productivity Conference
2nd FINPRO - Finance and Productivity Conference A conference jointly organised by the Competitiveness Research Network (CompNet), the European Bank for Reconstruction and…
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7th CompNet Annual Conference
Economic Growth, Trade and Productivity Dispersion 7 th CompNet Annual Conference, June 21-22, 2018, Leopoldina, Halle (Saale), Germany The main target of this conference was to…
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CompNet-EBRD Workshop
Localization and Productivity CompNet-EBRD Workshop, October 8-9, 2018, European Bank for Reconstruction and Development, London, United Kingdom The workshop of The…
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Das IWH auf der Jahrestagung des Vereins für Socialpolitik 2019 "30 Jahre Mauerfall" - Demokratie und Marktwirtschaft
IWH-BROWN-BAG-PANEL "Ost-West-Produktivitätslücke: Ursachen und Folgen" Ostdeutschlands Wirtschaft konnte anfänglich ihre Produktivität gegenüber den westdeutschen Verhältnissen…
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Too Poor to Be Green? The Effects of Wealth on the Residential Heating Transformation
Tobias Berg, Ulf Nielsson, Daniel Streitz
SSRN Working Paper,
2024
Abstract
Using the near-universe of Danish owner-occupied residential houses, we show that an exogenous increase in wealth significantly increases the likelihood to switch to green heating. We estimate an elasticity of one at the median of the wealth distribution, i.e., a 10% increase in wealth increase raises green heating adoption by 10%. Effects are heterogeneous along the wealth distribution: all else equal, a redistribution of wealth from rich households to poor households can significantly increase green heating adoption. We further explore potential channels of our findings (pro-social preferences, financial constraints, and luxury goods interpretation). Our results emphasize the role of economic growth for the green transition.
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Mission
The Halle Spirit We provide independent research on economic topics that really matter and aim to enrich society with facts and evidence-based insights that facilitate better…
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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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Gebietsstands-Transformation Deutschland
Schlüsselbrücken zur Gebietsstands-Transformation in Deutschland Der Staat besitzt die Möglichkeit, innerhalb seiner Staatsgrenzen die ursprüngliche räumliche Struktur seiner…
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Daten
Schlüsselbrücken zur Gebietsstands-Transformation in Deutschland – Daten Zur Demonstration, in welcher Form die Daten aufbereitet und angeboten werden, stellen wir aus den…
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PhD Graduates Financial Markets
PhD Graduates of the Department of Financial Markets Eleonora Sfrappini: "Four Essays on Banking, Climate Risks and Financial Regulation" (2024) Willam McShane: "The Competitive…
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