Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: C6.FDhP-1kWQt0c_2x+K~E'HI?U]#[7${.*W4xm\9DhK[Nr%|C-%g=R+B

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 19

WP(25/2026)519. Biodiversity-oriented forest management aligns with societal preferences across Europe

Autorzy: Giergiczny Marek, Jacobsen Jette Bredahl, Glenk Klaus, Meyerhoff Jürgen, Abildtrup Jens, Taye Fitalew Agimass, Budziński Wiktor, Czajkowski Mikołaj, Draus Borys, Faccioli Michela, Gajderowicz Tomasz, Getzner Michael, Hackbarth Tom X., Janiec Piotr, Lundhede Thomas, Mayer Marius, McVittie Alistair, Oh Rachel, Olschewski Roland, Pereira Henrique M., Quaas Martin, Socha Jarosław, Strange Niels, Ščasný Milan, Valasiuk Sviataslau, Wasiak Adam, Fernández Néstor
Forests across Europe are undergoing rapid transformation as biodiversity loss and climate change reshape priorities for forest management. Strategies that promote biodiverse and structurally complex forests are increasingly advocated to enhance ecol…

WP519. Biodiversity-oriented forest management aligns with societal preferences across Europe

Forests across Europe are undergoing rapid transformation as biodiversity loss and climate change reshape priorities for forest management. Strategies that promote biodiverse and structurally complex forests are increasingly advocated to enhance ecological resilience, yet they are often assumed to conflict with societal preferences, potentially limiting public support for conservation and restoration. We examined whether forest characteristics associated with biodiversity conservation and climate adaptation are consistently valued by society across Europe. We combined a visual discrete choice experiment involving 11,622 respondents from twelve European countries with a travel cost model to evaluate whether stated societal preferences were reflected in observed recreational behaviour. We then integrated harmonized preference estimates with continental forest inventory and remote-sensing data to map the spatial distribution of societal values associated with forest ecosystem condition. Across all countries, respondents consistently preferred forests characterized by taller canopies, greater tree-species richness, heterogeneous age structures, and more deadwood. These preferences closely aligned with observed visitation behaviour despite substantial differences in geography, accessibility, and recreational traditions. Spatial analyses further showed that forests with greater structural complexity consistently exhibited higher societal value. Our findings demonstrate that biodiversity-oriented forest management and societal values largely converge across Europe, suggesting that many interventions promoting ecological resilience can simultaneously strengthen public support for forest conservation and restoration, providing a stronger foundation for adaptive, multifunctional forest governance under global environmental change.


DOI: https://doi.org/10.33138/2957-0506.2026.25.519
Giergiczny Marek, Jacobsen Jette Bredahl, Glenk Klaus, Meyerhoff Jürgen, Abildtrup Jens, Taye Fitalew Agimass, Budziński Wiktor, Czajkowski Mikołaj, Draus Borys, Faccioli Michela, Gajderowicz Tomasz, Getzner Michael, Hackbarth Tom X., Janiec Piotr, Lundhede Thomas, Mayer Marius, McVittie Alistair, Oh Rachel, Olschewski Roland, Pereira Henrique M., Quaas Martin, Socha Jarosław, Strange Niels, Ščasný Milan, Valasiuk Sviataslau, Wasiak Adam, Fernández Néstor
biodiversity conservation forest management structural complexity forest recreation ecosystem services discrete choice experiment travel cost method

WP(13/2023)420. From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement

Autorzy: Chlebus Marcin, Nowak Artur
Algorithmic trading has been a central theme in numerous research papers, combining knowledge from the fields of Finance and Mathematics. This thesis aimed to apply basic Technical Analysis indicators for predicting price movement of three noble meta…

WP420. From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement

Algorithmic trading has been a central theme in numerous research papers, combining knowledge from the fields of Finance and Mathematics. This thesis aimed to apply basic Technical Analysis indicators for predicting price movement of three noble metals: Gold, Silver, and Platinum in a form of multi-class classification. That task was performed using four algorithms: Logistic Regression, k-Nearest Neighbors, Random Forest and XGBoost. The study incorporated feature filtering methods such as Kendall-tau filtering and PCA, as well as five different data frequencies: 1, 5, 10, 15 and 20 trading days. From a total of 40 potential models for each metal, the best one was selected and evaluated using data from period 2018-2022. The result revealed that models utilizing only Technical Analysis indicators were able to predict price movements to a significant extent, leading to investment strategies that outperformed the market in two out of three cases.


Chlebus Marcin, Nowak Artur
precious metals algotrading machine learning multiclass classification logistic regression nearest neighbors random forest xgboost

WP(23/2021)371. Application of machine learning in quantitative investment strategies on global stock markets

Autorzy: Grudniewicz Jan, Ślepaczuk Robert
The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Ra…

WP371. Application of machine learning in quantitative investment strategies on global stock markets

The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500 and selected CEE indices in the period between 2002-01-01 to 2020-10-30. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Quality of estimation was evaluated on independent subsets and with the use of sensitivity analysis. The research results indicated that quantitative strategies generate better risk adjusted returns than passive strategies and that for the analysed indices predominantly Bayesian Generalized Linear Model and Naïve Bayes were the best performing models. More comprehensive rank approach based on the results for all analysed models and indices allowed to select Bayesian Generalized Linear Model as the model which on average generated the best results.


Grudniewicz Jan, Ślepaczuk Robert
quantitative investment strategies machine learning neural networks regression trees random forests support vector machine technical analysis equity stock indices developed and emerging markets information ratio

WP(22/2021)370. Predicting football outcomes from Spanish league using machine learning models

Autorzy: Lewandowski Michał, Chlebus Marcin
High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with histori…

WP370. Predicting football outcomes from Spanish league using machine learning models

High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with historical forecasts extracted from bookmakers, which knowledge is commonly considered to be deep and high-quality. The aim of the paper was to design models with the highest possible predictive performances, get results close to bookmakers or even building better estimators. The work included detailed feature engineering based on previous achievements of this domain and own proposals. A built and selected set of variables was used with four machine learning methods, namely Random Forest, AdaBoost, XGBoost and CatBoost. The algorithms were compared based on: Area Under the Curve (AUC) and Ranked Probability Score (RPS). RPS was used as a benchmark in the comparison of estimated probabilities from trained models and forecasts from bookmakers' odds. For a deeper understanding and explanation of the demonstrated methods, which are considered as black-box approaches, Permutation Feature Importance (PFI) was used to evaluate the impacts of individual variables. Features extracted from bookmakers odds’ occurred the most important in terms of PFI. Furthermore, XGBoost achieved the best results on the validation set (RPS equals 0.1989), which obtained similar predictive power to bookmakers' odds (their RPS between 0.1977 and 0.1984). Results of the trained estimators were promising and this article showed that competition with bookmakers is possible using demonstrated techniques.


Lewandowski Michał, Chlebus Marcin
predicting football outcomes machine learning betting adaboost random forest xgboost catboost ranked probability score auc permutation feature importance

WP(9/2021)357. Are Transboundary Nature Protected Areas International Public Goods and Why People Think They Are (Not)? Hybrid Modelling Evidence from the EU Outer Borders

Autorzy: Valasiuk Sviataslau, Czajkowski Mikołaj, Giergiczny Marek, Żylicz Tomasz, Veisten Knut, Landa Mata Iratxe, Halse Askill Harkjerr, Angelstam Per
Former studies have shown that transboundary nature protected areas are not perceived as pure international public goods by citizens in neighbouring countries that share national parks. In this study, we assess what drives the valuation of nature pro…

WP357. Are Transboundary Nature Protected Areas International Public Goods and Why People Think They Are (Not)? Hybrid Modelling Evidence from the EU Outer Borders

Former studies have shown that transboundary nature protected areas are not perceived as pure international public goods by citizens in neighbouring countries that share national parks. In this study, we assess what drives the valuation of nature protection on the other side of the border in two European transboundary nature areas, the Białowieża Forest and Fulufjället. Applying hybrid choice modelling, we account for people’s attitudes when eliciting their preferences towards transboundary nature protected areas, and examine the impact of attitudes on the degree to which those preferences are consistent with the international public good hypothesis. We found that the intention of visiting the foreign part of the transboundary area, appreciation of transboundary justice and altruism, were the main drivers, whereas suspicious attitude towards the neighbouring country, propensity to free-ride, and manifestations of ‘patriotism’ applied as international public good mitigators to a limited degree only. Value of an extending the protection regime abroad was still positive for Scandinavians, whilst for Polish and Belarusian respondents a policy aiming at extending the protection abroad would lead to loss of human welfare. Facilitating visits of the foreign part by enhancing cross-border access can be expected to shift peoples’ preferences towards transboundary co-operation.


Valasiuk Sviataslau, Czajkowski Mikołaj, Giergiczny Marek, Żylicz Tomasz, Veisten Knut, Landa Mata Iratxe, Halse Askill Harkjerr, Angelstam Per
International public goods national parks forest transboundary nature protected areas public preferences willingness to pay discrete choice experiment hybrid modeling

WP(5/2021)353. The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw

Autorzy: Siwicki Dawid
The principal aim of this paper is to investigate the potential of machine learning algorithms in context of predicting housing prices. The most important issue in modelling spatial data is to consider spatial heterogeneity that can bias obtained res…

WP353. The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw

The principal aim of this paper is to investigate the potential of machine learning algorithms in context of predicting housing prices. The most important issue in modelling spatial data is to consider spatial heterogeneity that can bias obtained results when is not taken into consideration. The purpose of this research is to compare prediction power of such methods: linear regression, artificial neural network, random forest, extreme gradient boosting and spatial error model. The evaluation was conducted using train, validation, test and k-Fold Cross-Validation methods. We also examined the ability of the above models to identify spatial dependencies, by calculating Moran's I for residuals obtained on in-sample and out-of-sample data.


Siwicki Dawid
spatial analysis machine learning housing market random forest gradient boosting

WP(15/2020)321. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

Autorzy: Chlebus Marcin, Osika Zuzanna
The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to …

WP321. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to use boosting algorithms, which have not been used before to predict the response for the campaign and to use Explainable AI (XAI) methods to evaluate model's performance. The paper tries to examine whether 1) complex boosting algorithms perform better and 2) XAI tools are better indicators of models' performance than commonly used discriminatory power's measures like AUC. Portuguese bank telemarketing dataset was used with five machine learning algorithms, namely Random Forest (RF), AdaBoost, GBM, XGBoost and CatBoost, which were then later compared based on their AUC and XAI tools analysis – Permutated Variable Importance and Partial Dependency Profile. Two best performing models based on their AUC were XGBoost and CatBoost, with XGBoost having slightly higher AUC. Then, these models were examined using PDP and VI, which resulted in discovery of XGBoost potenitial overfitting and choosing CatBoost over XGBoost. The results show that new boosting models perform better than older models and that XAI tools could be helpful with models' comparisons.


Chlebus Marcin, Osika Zuzanna
direct marketing telemarketing relationship marketing data mining machine learning random forest adaboost gbm catboost xgboost bank marketing XAI variable importance partial dependency profile

WP(10/2017)239. Is Forest Landscape Restoration Socially Desirable? A Discrete Choice Experiment Applied to the Scandinavian Transboundary Fulufjället National Park Area

Autorzy: Valasiuk Sviataslau, Czajkowski Mikołaj, Giergiczny Marek, Żylicz Tomasz, Veisten Knut, Halse Askill Harkjerr, Landa Mata Iratxe, Elbakidze Marine, Angelstam Per
Landscape restoration can improve functionality of land cover patches as green infrastructure, which is essential to ensure the provision of a diverse range of ecosystem services. However, so far designation of protected areas in Fennoscandia has foc…

WP239. Is Forest Landscape Restoration Socially Desirable? A Discrete Choice Experiment Applied to the Scandinavian Transboundary Fulufjället National Park Area

Landscape restoration can improve functionality of land cover patches as green infrastructure, which is essential to ensure the provision of a diverse range of ecosystem services. However, so far designation of protected areas in Fennoscandia has focused primarily on remnant patches of naturally dynamic forests, and not on landscape level restoration. We applied stated preference methodology to assess citizens' preferences for forest landscape restoration in a cross-border region primarily managed for the industrial forestry, and – at the same time – hosting the transboundary Fulufjället National Park, shared between Sweden and Norway. There is scope for improving green infrastructure functionality by landscape restoration in adjacent forest areas, and including them into the National Park, which aims at gradual restoration of natural processes. In both countries, 54% and 55% choices made, respectively, indicated willingness to pay for extending the National Park by some area of forest landscape restoration.


DOI: https://doi.org/10.26405/WP/WNE/2017/239/010
Valasiuk Sviataslau, Czajkowski Mikołaj, Giergiczny Marek, Żylicz Tomasz, Veisten Knut, Halse Askill Harkjerr, Landa Mata Iratxe, Elbakidze Marine, Angelstam Per
stated preference valuation passive protection transboundary nature protected areas naturally dynamics boreal forests willingness-to-pay

WP(17/2016)208. Using geographically weighted choice models to account for spatial heterogeneity of preferences

Autorzy: Budziński Wiktor, Campbell Danny, Czajkowski Mikołaj, Demšar Urška, Hanley Nick
In this paper we investigate the prospects of using geographically weighted choice models for modelling of spatially clustered preferences. The data used in this study comes from a discrete choice experiment survey regarding public preferences for th…

WP208. Using geographically weighted choice models to account for spatial heterogeneity of preferences

In this paper we investigate the prospects of using geographically weighted choice models for modelling of spatially clustered preferences. The data used in this study comes from a discrete choice experiment survey regarding public preferences for the implementation of a new country-wide forest management and protection program in Poland. We combine it with high-resolution geographical information system data related to local forest characteristics. Using locally estimated discrete choice models we obtain location-specific estimates of willingness to pay (WTP). Variation in these estimates is explained by the socio-demographic characteristics of respondents and characteristics of the forests in their place of residence. The results are compared with those obtained from a more typical, two stage procedure which uses Bayesian posterior means of the mixed logit model random parameters to calculate individual-specific estimates of WTP. The latter approach, although easier to implement and more common in the literature, does not explicitly assume any spatial relationship between individuals. In contrast, the geographically weighted approach differs in this aspect and can provide additional insight on spatial patterns of individuals’ preferences. Our study shows that although the geographically weighted discrete choice models have some advantages, it is not without drawbacks, such as the difficulty and subjectivity in choosing an appropriate bandwidth. We find a number of notable differences in WTP estimates and their spatial distributions. At the current level of development of the two techniques, we find mixed evidence on which approach gives the better results.


Budziński Wiktor, Campbell Danny, Czajkowski Mikołaj, Demšar Urška, Hanley Nick
discrete choice experiment contingent valuation willingness to pay spatial heterogeneity of preferences forest management passive protection litter tourist infrastructure mixed logit geographically weighted model weighted maximum likelihood local maximum likelihood

WP(34/2015)182. Selling timber in Poland

Autorzy: Giergiczny Marek, Nehrebecka Natalia, Żylicz Tomasz
The paper looks at how the foresters try to reconcile sustainable management practices with economic viability of their operations within the legal framework they operate in. In particular, we compare prices received by the State Forest Enterprise in…

WP182. Selling timber in Poland

The paper looks at how the foresters try to reconcile sustainable management practices with economic viability of their operations within the legal framework they operate in. In particular, we compare prices received by the State Forest Enterprise in Poland in two types of timber auctions: constrained and unconstrained ones. While the latter allow for higher revenues, the former are maintained for political reasons. The authors verify alternative hypotheses regarding market behaviour of timber buyers. The data from timber auctions in 2011-2012 demonstrate that winning prices are determined by starting prices and by the market power of the State Forest Enterprise (diversified with respect to specific timber varieties), as well as by price expectations. Based on the modelling results the paper makes policy recommendations with respect to the design of timber auctions and – more broadly – with respect to striking a balance between timber and non-timber benefits from the forestry in Poland.


Giergiczny Marek, Nehrebecka Natalia, Żylicz Tomasz
Timber markets Public forests Multi-unit auction Sustainable management
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