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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12.03.2024 • 8/2024
Risk in the banking sector: four out of ten top supervisors come from the financial industry
Europe's banks realise excess returns on the stock market when their alumni join the boards of national supervisory authorities. A study by the Halle Institute for Economic Research (IWH) shows that this happens more frequently than previously recognised. The findings indicate a risk to financial stability and call for a more merit-based, transparent appointment of senior regulators.
Michael Koetter
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Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach
June Cao, Zhanzhong Gu, Iftekhar Hasan
Journal of International Accounting Research,
No. 3,
2023
Abstract
This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research.
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Long-run Competitive Spillovers of the Credit Crunch
William McShane
IWH Discussion Papers,
No. 10,
2023
Abstract
Competition in the U.S. appears to have declined. One contributing factor may have been heterogeneity in the availability of credit during the financial crisis. I examine the impact of product market peer credit constraints on long-run competitive outcomes and behavior among non-financial firms. I use measures of lender exposure to the financial crisis to create a plausibly exogenous instrument for product market credit availability. I find that credit constraints of product market peers positively predict growth in sales, market share, profitability, and markups. This is consistent with the notion that firms gained at the expense of their credit constrained peers. The relationship is robust to accounting for other sources of inter-firm spillovers, namely credit access of technology network and supply chain peers. Further, I find evidence of strategic investment, i.e. the idea that firms increase investment in response to peer credit constraints to commit to deter entry mobility. This behavior may explain why temporary heterogeneity in the availability of credit appears to have resulted in a persistent redistribution of output across firms.
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