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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Is Risk the Fuel of the Business Cycle? Financial Frictions and Oil Market Disturbances
Christoph Schult
IWH Discussion Papers,
Nr. 4,
2024
Abstract
I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle.
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Tracking Weekly State-Level Economic Conditions
Christiane Baumeister, Danilo Leiva-León, Eric Sims
Review of Economics and Statistics,
im Erscheinen
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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Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach
June Cao, Zhanzhong Gu, Iftekhar Hasan
Journal of International Accounting Research,
Nr. 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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Committing to Grow: Privatizations and Firm Dynamics in East Germany
Ufuk Akcigit, Harun Alp, André Diegmann, Nicolas Serrano-Velarde
IWH Discussion Papers,
Nr. 17,
2023
Abstract
This paper investigates a unique policy designed to maintain employment during the privatization of East German firms after the fall of the Iron Curtain. The policy required new owners of the firms to commit to employment targets, with penalties for non-compliance. Using a dynamic model, we highlight three channels through which employment targets impact firms: distorted employment decisions, increased productivity, and higher exit rates. Our empirical analysis, using a novel dataset and instrumental variable approach, confirms these findings. We estimate a 22% points higher annual employment growth rate, a 14% points higher annual productivity growth, and a 3.6% points higher probability of exit for firms with binding employment targets. Our calibrated model further demonstrates that without these targets, aggregate employment would have been 15% lower after 10 years. Additionally, an alternative policy of productivity investment subsidies proved costly and less effective in the short term.
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Evidence-based Support for Adaptation Policies in Emerging Economies
Maximilian Banning, Anett Großmann, Katja Heinisch, Frank Hohmann, Christian Lutz, Christoph Schult
IWH Studies,
Nr. 2,
2023
Abstract
In recent years, the impacts of climate change become increasingly evident, both in magnitude and frequency. The design and implementation of adequate climate adaptation policies play an important role in the macroeconomic policy discourse to assess the impact of climate change on regional and sectoral economic growth. We propose different modelling approaches to quantify the socio-economic impacts of climate change and design specific adaptations in three emerging market economies (Kazakhstan, Georgia and Vietnam) which belong to the areas that are heavily exposed to climate change. A Dynamic General Equilibrium (DGE) model has been used for Vietnam and economy-energy-emission (E3) models for the other two countries. Our modelling results show how different climate hazards impact the economy up to the year 2050. Adaptation measures in particular in the agricultural sector have positive implications for the gross domestic product (GDP). However, some adaptation measures can even increase greenhouse gas emissions. In addition, the focus on GDP as the main indicator to evaluate policy measures can produce welfare-reducing policy decisions.
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Political Ideology and International Capital Allocation
Elisabeth Kempf, Mancy Luo, Larissa Schäfer, Margarita Tsoutsoura
Journal of Financial Economics,
Nr. 2,
2023
Abstract
Does investors’ political ideology shape international capital allocation? We provide evidence from two settings—syndicated corporate loans and equity mutual funds—to show ideological alignment with foreign governments affects the cross-border capital allocation by U.S. institutional investors. Ideological alignment on both economic and social issues plays a role. Our empirical strategy ensures direct economic effects of foreign elections or government ties between countries are not driving the result. Ideological distance between countries also explains variation in bilateral investment. Combined, our findings imply ideological alignment is an important, omitted factor in models of international capital allocation.
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Postdoctoral Researcher in Entrepreneurship and Innovation (f/m/x, 100%) [2024-05]
Vacancy Postdoctoral Researcher in Entrepreneurship and...
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PostDoc Position in the Department of Laws, Regulations and Factor Markets (100%) (f/m/x) [2024-02]
Vacancy PostDoc Position (100%) (f/m/x) [2024-02] ...
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Professor in Finance and Labor in conjunction with a position as Senior Research Advisor at the Department of Laws, Regulations and Factor Market
Vacancy Professor in Finance and Labor in conjunction with a...
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