Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

Please send your paper by e-mail: ewgtH`6~@s=b*j8Yar\Ch.lqQP7]#[]gZ`.OxoxdzUx%0HG{R7qnQgZA~ 

Please send 2 files:

(1) the main text without the title of the article and the authors (DOC/DOCX file) and

(2) the title page including: the title of the paper, the authors and their affiliation (DOC/DOCX file).

Please read the detailed editing requirements before submitting your text. 

Number of Results 2

WP(30/2025)493. Is smooth Energiewende possible? Improving the performance of climate policies in Germany by optimizing the risk of electricity delivery

Authors: Bandurski Jakub, Hałatek Eliza, Łaziński Adam, Künstler Michał
The Energiewende is a deep-rooted notion in the German economy. The main goal is to achieve climate neutrality by transitioning to renewable energy sources. However, the feasibility of this transition is partially hindered by power grid congestion, w…

WP493. Is smooth Energiewende possible? Improving the performance of climate policies in Germany by optimizing the risk of electricity delivery

The Energiewende is a deep-rooted notion in the German economy. The main goal is to achieve climate neutrality by transitioning to renewable energy sources. However, the feasibility of this transition is partially hindered by power grid congestion, which undermines system efficiency and leads to both economic and environmental costs. We address this issue by making a prediction of the likelihood of congestion occurrence within the German TenneT DE electricity network in the years 2020-2023. We propose a twofold approach offering a combination of advanced econometric models and state-of-the-art machine learning methods. We offer separate solutions for up congestion when additional energy needs to be pushed to the network as well as down congestion when energy needs to be pulled away from the network. Analyzing the CatBoost with XAI, we identify factors that play a significant role in driving redispatch events within the German electricity network.


DOI: https://doi.org/10.33138/2957-0506.2025.30.493
Bandurski Jakub, Hałatek Eliza, Łaziński Adam, Künstler Michał
energiewende econometrics machine learning climate policy catboost

WP(30/2020)336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

Authors: Heba Mateusz, Chlebus Marcin
The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the…

WP336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the prediction of companies bankruptcy. Some authors of scientific papers claim that the process of evaluation of the companies situation requires comparison of its characteristics defined as financial ratio with situation of whole sector in order to obtain reliable conclusions. In this paper, a hypothesis that usage of the industry benchmarks (transformation of raw financial ratios values into sectoral deciles groups numbers) improves results of bankruptcy prediction logistic regression model is verified. Based on empirical results for Polish market, it turns out that although models estimated on different types of data have similar discriminatory power, logistic regression using raw financial ratios obtained a bit better results than its industry equivalents defined as sectoral deciles groups numbers. It is worth emphasizing that empirical part of paper uses information about 109K companies what is the rarity in bankruptcy prediction papers – researchers usually use small datasets that include less than several hundred records.


Heba Mateusz, Chlebus Marcin
bankruptcy prediction financial ratios industry financial ratios sectoral financial ratios logistic regression financial econometrics
  • 1