Climate Stress Tests, Bank Lending, and the Transition to the Carbon-neutral Economy
Larissa Fuchs, Huyen Nguyen, Trang Nguyen, Klaus Schaeck
IWH Discussion Papers,
No. 9,
2024
Abstract
We ask if bank supervisors’ efforts to combat climate change affect banks’ lending and their borrowers’ transition to the carbon-neutral economy. Combining information from the French supervisory agency’s climate pilot exercise with borrowers’ emission data, we first show that banks that participate in the exercise increase lending to high-carbon emitters but simultaneously charge higher interest rates. Second, participating banks collect new information about climate risks, and boost lending for green purposes. Third, receiving credit from a participating bank facilitates borrowers’ efforts to improve environmental performance. Our findings establish a hitherto undocumented link between banking supervision and the transition to net-zero.
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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,
No. 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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IWH Subsidy Database
IWH Subsidy Databse The microdatabase currently comprises nine data sets on direct business subsidy programmes in Germany. The programme statistics kept by the project sponsors…
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Research Clusters
Three Research Clusters Research Cluster "Economic Dynamics and Stability" Research Questions This cluster focuses on empirical analyses of macroeconomic dynamics and stability.…
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Homepage
IWH founds a European centre for microdata research The Halle Institute is once again growing significantly. Its new "Centre for Business and Productivity Dynamics" aims to…
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Economic Outlook
IWH Economic Outlook 2025 Frosty prospects for the German economy December 12, 2024 The German economy will continue to stagnate in winter 2024/2025. Industry is suffering from a…
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Transformation tables for administrative borders in Germany
Transformation tables for administrative borders in Germany The state has the ability to change the original spatial structure of its administrative regions. The stated goal of…
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Does IFRS Information on Tax Loss Carryforwards and Negative Performance Improve Predictions of Earnings and Cash Flows?
Sandra Dreher, Sebastian Eichfelder, Felix Noth
Journal of Business Economics,
January
2024
Abstract
We analyze the usefulness of accounting information on tax loss carryforwards and negative performance to predict earnings and cash flows. We use hand-collected information on tax loss carryforwards and corresponding deferred taxes from the International Financial Reporting Standards tax footnotes for listed firms from Germany. Our out-of-sample tests show that considering accounting information on tax loss carryforwards does not enhance performance forecasts and typically even worsens predictions. The most likely explanation is model overfitting. Besides, common forecasting approaches that deal with negative performance are prone to prediction errors. We provide a simple empirical specification to account for that problem.
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Searching where Ideas Are Harder to Find – The Productivity Slowdown as a Result of Firms Hindering Disruptive Innovation
Richard Bräuer
IWH Discussion Papers,
No. 22,
2023
Abstract
This paper proposes to explain the productivity growth slowdown with the poaching of disruptive inventors by firms these inventors threaten with their research. I build an endogenous growth model with incremental and disruptive innovation and an inventor labor market where this defensive poaching takes place. Incremental firms poach more as they grow, which lowers the probability of disruption and makes large incremental firms even more prevalent. I perform an event study around disruptive innovations to confirm the main features of the model: Disruptions increase future research productivity, hurt incumbent inventors and raise the probability of future disruption. Without disruption, technology classes slowly trend even further towards incrementalism. I calibrate the model to the global patent landscape in 1990 and show that the model predicts 52% of the decline of disruptive innovation until 2010.
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CompNet Database
The CompNet Competitiveness Database The Competitiveness Research Network (CompNet) is a forum for high level research and policy analysis in the areas of competitiveness and…
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