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Boris Kozyrev

Boris Kozyrev
Aktuelle Position

seit 9/21

Wissenschaftlicher Mitarbeiter der Abteilung Makroökonomik

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

Forschungsschwerpunkte
  • Makroökonomik
  • Prognosen
  • angewandte Ökonometrie

Seit September 2021 ist Boris Kozyrev Doktorand in der Abteilung Makroökonomik. Seine Forschungsinteressen liegen in der angewandten Ökonometrie, Makroökonomik sowie Prognosen.

Boris Kozyrev studierte an der Lomonosov Moscow State University und der Toulouse School of Economics. Bevor er zum IWH kam, war er Junior Researcher bei der Russian Presidential Academy of National Economy and Public Administration.

Ihr Kontakt

Boris Kozyrev
Boris Kozyrev
- Abteilung Makroökonomik
Nachricht senden +49 345 7753-865

Publikationen

Zitationen
2

Arbeitspapiere

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Forecast Combination and Interpretability Using Random Subspace

Boris Kozyrev

in: IWH Discussion Papers, Nr. 21, 2024

Abstract

<p>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.</p>

Publikation lesen
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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

in: 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.

Publikation lesen
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