Forecast Combination and Interpretability Using Random Subspace
Boris Kozyrev
IWH Discussion Papers,
No. 21,
2024
Abstract
This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.
Read article
Who Benefits from Place-based Policies? Evidence from Matched Employer-Employee Data
Philipp Grunau, Florian Hoffmann, Thomas Lemieux, Mirko Titze
IWH Discussion Papers,
No. 11,
2024
Abstract
We study the wage and employment effects of a German place-based policy using a research design that exploits conditionally exogenous EU-wide rules governing the program parameters at the regional level. The place-based program subsidizes investments to create jobs with a subsidy rate that varies across labor market regions. The analysis uses matched data on the universe of establishments and their employees, establishment-level panel data on program participation, and regional scores that generate spatial discontinuities in program eligibility and generosity. These rich data enable us to study the incidence of the place-based program on different groups of individuals. We find that the program helps establishments create jobs that disproportionately benefit younger and less-educated workers. Funded establishments increase their wages but, unlike employment, wage gains do not persist in the long run. Employment effects estimated at the local area level are slightly larger than establishment-level estimates, suggesting limited spillover effects. Using subsidy rates as an instrumental variable for actual subsidies indicates that it costs approximately EUR 25,000 to create a new job in the economically disadvantaged areas targeted by the program.
Read article
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.
Read article
Herding Behavior and Systemic Risk in Global Stock Markets
Iftekhar Hasan, Radu Tunaru, Davide Vioto
Journal of Empirical Finance,
September
2023
Abstract
This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.
Read article
Wage and Employment Effects of Insolvencies
Wage and employment effects of bankruptcies Although the consequences of bankruptcies for affected employees are frequently debated in the public (e.g. due to the bankruptcy of…
See page
Firm Surveys
Firm Surveys Since 1993, IWH has conducted regular surveys among a fixed group of firms in the manufacturing and construction industry to collect individual data on economic…
See page
Financial Stability
Financial Systems: The Anatomy of the Market Economy How the financial system is constructed, how it works, how to keep it fit and what good a bit of chocolate can do. Dossier In…
See page
IWH Construction Survey
IWH Construction Survey From 1993 until the first quarter of 2017, the IWH conducted regular surveys among companies. The results of these surveys could be used to promptly…
See page
IWH Industry Survey
IWH Industry Survey From 1993 until the first quarter of 2017, the IWH conducted regular surveys among companies. The results of these surveys could be used to promptly describe…
See page
Projects
Our Projects 07.2022 ‐ 12.2026 Evaluation of the InvKG and the federal STARK programme On behalf of the Federal Ministry of Economics and Climate Protection, the IWH and the RWI…
See page
