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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MICROPROD
MICROPROD Raising EU Productivity: Lessons from Improved Micro Data The goal of MICROPROD is to contribute to a greater understanding of the challenges brought about in Europe by…
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DPE Course Programme Archive
DPE Course Programme Archive 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2025 Microeconomics several lecturers winter term 2024/2025 (IWH) Econometrics…
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Lecturers
Lecturers at CGDE Institutions Jordan Adamson Assistant Professor at Institute for Empirical Economic Research, Leipzig University. Website Course: Econometrics (winter term…
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Tracking Weekly State-Level Economic Conditions
Christiane Baumeister, Danilo Leiva-León, Eric Sims
Review of Economics and Statistics,
No. 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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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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Centre for Business and Productivity Dynamics
Centre for Business and Productivity Dynamics (IWH-CBPD) The Centre for Business and Productivity Dynamics (CBPD) was founded in January 2025 and works with policy and research…
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Research Groups
Our Research Groups Banking, Regulation, and Incentive Structures Data Science in Financial Economics Econometric Tools for Macroeconomic Forecasting and Simulation Education,…
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Is Risk the Fuel of the Business Cycle? Financial Frictions and Oil Market Disturbances
Christoph Schult
IWH Discussion Papers,
No. 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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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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