Econometrics. Ekonometria. Advances in Applied Data Analysis
The Econometrics journal has been published continuously since 1996. The Econometrics is a scientific journal that promotes both theoretical aspects of mathematical, statistical and econometric methods as well as application possibilities. It focuses on the presentation of new and modernization of well-known methods as well as their adaptation in economy, business processes and social sciences in a very broad meaning. Due to the fast publishing production, the presented articles refer to the latest research, papers are always up-to-date, original, scientific, never printed before in other scientific journals. ISSN 1507-3866 e-ISSN 2449-9994 The journal is under the patronage of the Committee of Statistics and Econometrics of the Polish Academy of Sciences. The official title of this journal is Econometrics. Ekonometria. Advances in Applied Data Analysis
Publishing House of Wroclaw University of Economics and Business
Analysis of the Impact of a Passive Interest in Sport on Student Lifestyle
Aim: The aim of the study was to determine the impact of a passive interest in sport on student lifestyle. Particular attention was given to analysing the relationship between the consumption of sports-related content and health behaviour, as well as attitudes toward physical activity. Methodology: Data were collected using a custom-designed survey questionnaire administered to 258 university students. The data analysis employed the chi-square independence test, correspondence analysis, and classification trees. Results: The study demonstrated that an interest in sport contributes to a healthier lifestyle among students, primarily by inspiring physical activity. Watching sports broadcasts and attending sports events as spectators proved to be significant determinants of a healthy lifestyle. A statistically significant, albeit weak, correlation between gender and level of interest in sport was also observed. Implications and recommendations: The findings suggest the need to promote the positive behavioural models exhibited by athletes, as these may motivate young individuals to adopt healthier lifestyles. It is also recommended to expand educational and media initiatives that encourage physical activity, especially among young people. Originality/value: This study provides new empirical data on the impact of the passive participation in sports culture on the daily health-related choices of young adults. It fills a gap in the literature regarding the social aspects of sports consumption and its influence on lifestyle behaviour.
Copyright (c) 2025 Dominik Głowacki https://creativecommons.org/licenses/by-sa/4.0
The Cumulative Residual Entropy in Assessing the Duration of Unemployment
Aim: The aim of the article was to use cumulative residual entropy (CRE) to assess the informational value of data on deregistration from the Poviat Labour Office in Szczecin (Poland) from 2007 to 2024. The events of declining cooperation with the labour office and starting work were analysed. Methodology: The study used survival analysis methods with the assumption of an exponential distribution of duration. The CRE was calculated for the specified distribution parameters. Hierarchical clustering was used to identify clusters of years with similar CRE values. Using the dynamic time warping method, the CRE time series and the unemployment rate were compared. Results: The study revealed a similarity between the registered unemployment rate and the development of entropy of unemployment duration. High unemployment rates corresponded to high entropy values, and vice versa. During periods of shock (caused by crises) in the labour market, the CRE for both reasons for deregistration took on extreme values. Implications and recommendations: Analyses related to the duration in unemployment are more informative when the unemployment rate is lower. The labour market, as a system, is then characterised by less uncertainty. Originality/value: There is a lack of research on the application of entropy by using survival analysis in studies regarding the labour market. The study took into account the relationship between the hazard value and the CRE for the exponential duration distribution.
Application of Econometric Techniques in Economics and Related Areas
Copyright (c) 2025 Beata Bieszk-Stolorz https://creativecommons.org/licenses/by-sa/4.0
Rethinking Scale in Applied Econometrics: Practical Impacts of Log Transformations on Model Performance
Aim: This study examines the practical impact of measurement scale choices, particularly logarithmic transformations, on the accuracy, reliability, and interpretability of econometric models using macroeconomic indicators from the MENA region. Methodology: Using real-world MENA data, multiple techniques (OLS, Fixed Effects, Random Effects, GMM) were applied with both raw and log-transformed variables to compare outcomes for variance stability, normality, stationarity, and heteroskedasticity. Results: Log transformations substantially improve model diagnostics by stabilising variance, enhancing normality, and reducing heteroskedasticity, yielding more precise estimates. However, they alter coefficient interpretation, highlighting a trade-off between statistical robustness and economic meaning. Implications and Recommendations: The findings underscore the critical need for appropriate data transformations to ensure valid and interpretable results, offering practical guidance for researchers and policymakers in model specification for policy analysis and forecasting. Originality/Value: This study provides a novel comparative exploration of transformation effects across methodologies and one of the few region-specific investigations using MENA data, delivering actionable insights for methodological and applied research.
Econometrics, Statistics, Mathematics - Methods and Methodology
Copyright (c) 2025 Chellai Fatih https://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of Household Debt Levels and Structures in European Countries: Trends and Implications
Aim: The aim of the article was to analyse the level and structure of household indebtedness in selected European countries over two periods: 2018–2019 and 2020–2022. The research focused on two key objectives: (1) a comparative analysis of the forms of indebtedness among households in different European countries, and (2) a comparative analysis of the euro area and non-euro area EU Member States with respect to the level and structure of household debt. Methodology: The study is based on secondary data from Eurostat concerning the financial resources and debt levels of European households. The analysis covered two distinct time periods (2018–2019 and 2020–2022) and compared the prevalence and structure of different debt instruments. The research included descriptive and comparative methods, examining household behaviour in euro area and non-euro area countries. Results: The analysis showed that there were no statistically significant differences between euro area and non-euro area households in terms of asset and debt categories. In the euro area, households more frequently used short-term and long-term loans, while they were less likely to invest in quoted shares and bonds. In non-euro area countries, short-term loans dominated, suggesting a stronger tendency to use quick access financing tools. Debt securities and long-term loans were less prevalent in these countries. Implications and recommendations: The findings suggest that household debt preferences were relatively stable across Europe, with minor regional differences. Policymakers should consider these patterns when designing financial literacy programmes and credit regulations. Future research could focus on the behavioural factors influencing household debt decisions and investigate the impact of macroeconomic changes on these trends. Originality/value: This study contributes to the literature by offering a comparative perspective on household indebtedness in euro area and non-euro area countries, using up-to-date Eurostat data across two relevant time frames. It provides insights into the structure of household liabilities and the regional differences in debt preferences.
Copyright (c) 2025 Małgorzata Grzywińska-Rąpca, Agnieszka Zawadzka https://creativecommons.org/licenses/by-sa/4.0
Analysis of Financial Speculators’ Herd Behaviour Impact on the Instability of Commodity Prices. Evidence from Weekly WTI Crude Oil Market Data Using Uncertainty Theory (2018–2024)
Aim: The purpose of this paper is to model the herd behavior of financial speculators in order to demonstrate its effect on the instability of commodity prices, particularly WTI crude oil prices, following recent events: the COVID-19 pandemic, the war in Ukraine and the war in the Middle East. Methodology: The authors approach relies on introducing uncertainty theory and VNM1 expected utility, rather than relying on ARDL modelling and dummy variables over the period 2018–2024, using weakly data provided by the CFTC. Results: Our findings indicate the significant effect of variables, buying and selling positions of financial speculators and the war in Ukraine, namely increasing mimetic behavior of traders’ and financial speculators’ impact on prices. This result is valuable for a short-term relationship; however, there is no long-term relationship, which means no co-integration which can be interpreted by the efficiency of WTI crude oil market in long term. Implications and recommendations: The prices of certain commodities have become unpredictable over the recent years, and the effect of fundamentals is becoming increasingly negligible in the face of speculators’ decisions on the financial markets. Hence the importance of taking stock of the impact of the mimetic behavior of financial speculators, and of regulating their activity. Originality/value: In contrast to earlier research, introducing the behaviour or psychological impact of recent events on speculative behaviour on the financial markets and, in turn, on the physical markets makes this study unique. Therefore, rather than focusing on the direct impact of financial speculation on oil prices, this study aims to capture the indirect influence. The statistical tool used was ARDL modelling combined with the theory of uncertainty and VNM expected utility theory in order to capture this indirect effect, originally linked to the risk aversion of the various market players. This feature is very important because it allows to predict more precisely future market behavior and trends.
Copyright (c) 2025 Tebache Djamal, Chetbani Saida https://creativecommons.org/licenses/by-sa/4.0
Reduction of Climate Risk as a Key to Business Performance – Framework for Sustainable Corporate Treasury Management (CTM)
Aim: The article aims to examine the impact of climate instability risk on Corporate Treasury Manage-ment (CTM) and demonstrate that sustainable strategies can effectively mitigate this risk while enhancing corporate financial performance. Methodology: A theoretical and empirical approach is employed, including econometric modelling, case study analysis, and literature review, with a focus on the use of financial instruments – particularly climate derivatives – within CTM. Results: Effective management of climate risk through sustainable policies and financial instruments raises treasury levels and strengthens corporate competitiveness by reducing unexpected losses and the costs of maintaining high liquidity reserves. Implications and recommendations: The study recommends incorporating environmentally conscious strategies into CTM and implementing fiscal incentives and regulatory frameworks to support climate risk hedging tools. Further research should explore sector-specific applications of sustainable CTM, especially in climate-sensitive industries. Originality/value: The article offers a novel integration of sustainability and treasury optimisation, showing that environmental responsibility can directly support and enhance core financial goals within CTM frameworks.
Copyright (c) 2025 Grzegorz Michalski https://creativecommons.org/licenses/by-sa/4.0
Household Production Satellite Account as an Integral Account for Estimating Value Added in the National Economy
Aim: Satellite accounts provide an integrated economic framework that complements the System of National Accounts (SNA) by capturing economic activities not fully represented in core statistics. This study presents the Household Production Satellite Account (HPSA) as an example of best practices in extending official statistics. The primary goal is to estimate the monetary value of domestic work and household production (market and non-market) in Poland, contributing to an extended GDP model. Methodology: Following the ESA 2010 methodology, this study applies the input and replacement cost methods to assess unpaid labour, using the specialist wage approach to estimate housework value. Results: In 2023, Poland’s GDP was PLN 3,410.1 billion, with non-SNA household production accountting for additional 46.5% to GDP. The value of domestic labour was PLN 1,605.9 billion, representing 79.9% of non-market household production. When using an extended economic framework, household production constituted 34.6-36.5% of the national economy. Implications and recommendations: The results highlight the substantial contribution of household production, often overlooked in economic assessments. Regular valuation of satellite accounts, as recommended by Eurostat and UNECE, would improve economic analysis. Integrating these accounts into national statistics provides a more comprehensive view of household contributions, reducing distortions in economic indicators. Originality/value: The most anticipated outcome of the analysis was the estimation of the gross value added of non-market household production and its comparison to the GDP for the total national economy across from 2003 to 2023.
Copyright (c) 2025 Marta Marszałek https://creativecommons.org/licenses/by-sa/4.0
Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study
Aim: An anomaly is an observation or a group of observations that is unusual for a given dataset. Anomaly detection has many applications, not only as a step of data preparation but also, for example, as a way of identifying credit card fraud detection, network intrusions and much more. There are diverse methods of anomaly detection. In particular two groups of methods have been developed independently – statistical methods and machine learning algorithms. Those methods are rarely compared. While statistical methods focus on formulating a measure of the abnormality of the observations, supervised machine learning makes it possible to use data about typical observations and previously identified anomalies. The aim of this paper was to compare the two approaches by conducting a simulation study. Methodology: A simulation study was conducted, during which the data was generated using copula functions. For the purpose of generating different types of anomalies, marginal distributions of the variables were manipulated. The effectiveness of each method was evaluated based on measures of classification model performance. Results: While the accuracy of the statistical methods was dependent on the precise prediction of the percentage of the anomalies that would occur in the data, the machine learning algorithms’ recall was significantly lower when the change in the marginal distribution of the value parameters was smaller. Implications and recommendations: For the statistical methods included in the study, knowledge about the distribution of the variables was crucial while the supervised machine learning algorithms required acquiring a training dataset. Unlike machine learning algorithms, the statistical methods performed with similar accuracy even when the change in the marginal distribution parameters’ value was smaller. Originality/value: The two approaches to anomaly detection presented in the paper are not often compared, usually used by two separate groups of researchers – statisticians and machine learning or data science specialists.
Copyright (c) 2025 Klaudia Lenart https://creativecommons.org/licenses/by-sa/4.0
Central Limit Theorem for Conditional Mode in the Single Functional Index Model with Data Missing at Random
This paper concentrates on nonparametrically estimating the conditional density function and conditional mode within the single functional index model for independent data, particularly when the variable of interest is affected by randomly missing data. This involves a semi-parametric single model structure and a censoring process on the variables. The estimator’s consistency (with rates) in a variety of situations, such as the framework of the single functional index model (SFIM) under the assumption of independent and identically distributed (i.i.d) data with randomly missing entries, as well as its performance under the assumption that the covariate is functional, are the main areas of focus. For this model, the nearly almost complete uniform convergence and rate of convergence are established. The rates of convergence highlight the critical part that the probability of concentration play in the law of the explanatory functional variable. Additionally, we establish the asymptotic normality of the derived estimators proposed under specific mild conditions, relying on standard assumptions in Functional Data Analysis (FDA) for the proofs. Finally, we explore the practical application of our findings in constructing confidence intervals for our estimators. The rates of convergence highlight the critical part that the probability of concentration play in the law of the explanatory functional variable.
Copyright (c) 2024 Anis Allal, Abdessamad Dib, Abbes Rabhi https://creativecommons.org/licenses/by-sa/4.0
Signaling Inclusivity: Unveiling the Determinants of Corporate LGBTQIA+ Inclusivity Through Signaling Theory
Aim: This study aims to explore how U.S. corporations signal their commitment to LGBTQ+ inclusivity, using signaling theory as a framework. Methodology: By analyzing a dataset of 700 companies and utilizing the Corporate Equality Index (CEI) from the Human Rights Campaign, the research examines the role of sector characteristics and voluntary participation in inclusivity efforts. Results: Through univariate analysis, instrumental variable (IV) regression, and Structural Equation Modeling (SEM), the study finds that consumer-facing sectors tend to signal a stronger commitment to inclusivity, reflected in higher CEI scores, while sectors with high injury risks show weaker or inverse correlations with inclusivity. Additionally, the findings highlight that the authenticity of inclusivity efforts – particularly voluntary reporting – is crucial, suggesting that genuine engagement, rather than mere compliance, enhances the effectiveness of inclusivity signaling. Implications and recommendations: The implications for researchers include a deeper understanding of how sector dynamics and voluntary actions influence corporate inclusivity, while practitioners are advised to prioritize authentic engagement to effectively communicate their inclusivity values to stakeholders. Originality/value: This research contributes to the literature by emphasizing the strategic importance of authentic inclusivity signaling in shaping corporate culture and external perceptions.
Copyright (c) 2025 Kinga Siuta https://creativecommons.org/licenses/by-sa/4.0
Public Support for Emigration Post-EU Accession: An LM Model Analysis
Aim: Since 2004 European countries, such as Germany and the United Kingdom, have noted an unusual upward trend of migrants from Central and Eastern Europe. The author aimed to examine the popular perception of emigration from Poland in the years following EU accession. Moreover, the study presents the effect of the observed socio-economic features (i.e. age, education, marital status, socio-professional status) affecting the conditional probabilities of the response variable, considering also the unobserved heterogeneity between the respondents representing Polish society. Methodology: The study was based on the Latent Markov (LM) models which allowed finding homogenous groups of respondents on the basis of their definite responses measured at different points in time. The author compared the respondents behaviour in the groups arriving just after EU accession, in relation to the other surveyed surges in migration using the Latent Markov models with different types of constraints. Then, to show the effect of observable socio-economic characteristics, taking into account the unobserved heterogeneity between the subjects, the study employed the version including the covariates in the measurement part of the model. Results: Based on the Polish longitudinal social survey, i.e. Social Diagnosis, the study shows that the analysed data can be explained by two latent states of Poles sharing the same propensity towards emigration. It was observed that Poles were more stable in their feelings concerning their approach to the emigration in the period 2011–2015 (following the first post-accession phase). Based on the available (time-constant and time-varying) covariates, the author demonstrated that certain types of people are more prone to migrate than others. Implications and recommendations: The presented approach could be applied to data from other economies – both within the EU and outside it – which have also seen massive outflows of people, to show the changing behaviour of society. This methodology might also be helpful with policy challenges and the effective management of migration, as well as for future data concerning the attitudes towards refugees from Ukraine coming to Poland at present. Originality/value: Unlike previous studies, the author assumed that the attitudes toward emigration can vary on the response variable, i.e. because of the unobserved covariate such as ‘patriotic behaviour’ (unobserved heterogeneity), which may change over time. The study applied the appropriate LM techniques extended to include the covariates in the measurement part of the LM model and the longitudinal weights of the survey. Note that the applied model also allowed achieving a better measure-of-fit compared to the standard random-effect model, and the parameters of the LM model additionally enabled to evaluate the dynamic of the latent trait.
Copyright (c) 2024 Ewa Genge https://creativecommons.org/licenses/by-sa/4.0
Default Prediction Using the Cox Regression Model and Macroeconomic Conditions – A Lifetime Perspective
Abstract Aim: Since the implementation of International Financial Reporting Standards 9 (IFRS 9), several techniques on estimating the risk parameters for calculating the expected credit losses (ECL) have been implemented across financial institutions. The purpose of this study was to present the advantages of using survival analysis for the estimation of the probability of default (PD) given the particularity of the method, within the estimation of the time up to an event occurring. Methodology: The Cox Proportional Hazard Rate was selected as the model to predict the default incorporating the time to event and the macroeconomic conditions into the model. At the end of this research a validation was performed of the accuracy of the survival method through the time. Results: The ROC curve and concordance statistics were evaluated on different time points, the survival model shows a consistent high discriminatory power in terms of the AUC over each time horizon. The results revealed that time dependent ROC curves for the selected years from 1 to 4 and the first year have the largest area under the curve (AUC). The time dependent curve is evaluated at all event times under the 95% pointwise confidence limits of the fitted model, the AUC was on average around 0.8, with the highest values in the first years. Implications and recommendations: The results are promising for PD estimation in a lifetime perspective. This method is accurate for IFRS9 ECL purposes as time varying internal (portfolio characteristics) and external (macroeconomic) factors can be incorporated. The dynamic model incorporates the variability and changes of the variables from the past up to now. Originality/value: To date the survival analysis techniques were used mostly for PD estimations but not in a IFRS9 ECL perspective. Given the nature of this method of estimating the remaining lifetime perspective and the inclusion into the model of the macro variables, this model can be considered adequate according to IFRS9. The paper aimed to present their uses for lifetime prediction.
Copyright (c) 2024 Aneta Ptak-Chmielewska, Juan Pablo Espinosa Gonzalez https://creativecommons.org/licenses/by-sa/4.0
A United Probabilistic Approach to Minimising the Sum of Absolute Values
Aim: Introduce a novel method for minimizing functions in the form of a sum of absolute values. Methodology: The sum of absolute values can be standardized so that the sum of the coefficients equals 1. In this case, the sum of absolute values takes the form 𝐸𝐸|𝑋𝑋−𝑎𝑎|, where 𝑋𝑋 is a random variable. Results: Any median of 𝑋𝑋 is a minimiser of the function 𝐸𝐸|𝑋𝑋−𝑎𝑎|. To minimise the function, it suffices to find any median of 𝑋𝑋. Implications and recommendations: The method introduced in this paper can be applied to minimise a large family of functions. Originality/value: Our work uses the probabilistic method to solve optimization problems.
Copyright (c) 2024 Changyong Feng, Honghong Liu, Ethan Poon, Ge Feng https://creativecommons.org/licenses/by-sa/4.0
Predicting Price Trends in the Wheat Market Using Technical Analysis Indicators
Aim: The aim of the study was to determine the trend of wheat prices using technical analysis indicators. Methodology: Selected technical analysis indicators were used to determine price trends. The parameters of two technical analysis indicators, modified from their default settings, were used to forecast price trends. The Parabolic Stop and Reversal (SAR) indicator, which is a valued trend indicator, was chosen. The second indicator used was the Relative Strength Index (RSI) oscillator, which is also popular with proponents of technical analysis. The data source used to forecast wheat price trends was information from the MetaTrader5 trading platform. Results: The research analysis of the applied strategies shows that it is possible to realistically predict price trends on the wheat quotation market. There were two forecasts of the future price movement of wheat based on the indications of the indicators: (1) an indication to go long (in which case the investor should not expect a change in the trend) and (2) an indication to go short (both the SAR indicator and the RSI indicated a possible change in the trend to the downside). Implications and recommendations: Based on the analysis conducted in the article, it was concluded that technical analysis tools are useful in predicting prices in the wheat futures market. The conducted analysis indicated that the application of technical analysis in predicting wheat futures prices is effective. Therefore, technical analysis indicators can be considered by investors as a tool to assist in making investment decisions in various markets, including the agricultural products market, and the use of technical analysis in price forecasting seems to be justified in the context of economic and geopolitical changes. Originality/value: Due to the development of research methods and tools, it can be assumed that today's investors are looking for alternatives that allow them to reduce the time needed to gather and analyse market data. The presented approach to forecasting market prices based on technical analysis indicators showed that it can be used by a wider range of market participants than fundamental analysis, which requires more extensive econometric knowledge.
Copyright (c) 2024 Paweł Oktaba, Małgorzata Grzywińska-Rąpca https://creativecommons.org/licenses/by-sa/4.0
Isolation Forests for Symbolic Data as a Tool for Outlier Mining
Aim: Outlier detection is a key part of every data analysis. Although many definitions of outliers can be found in the literature, all of them emphasize that outliers are objects that are in some way different from other objects in the dataset. Many different approaches have been proposed, compared, and analyzed for the classical data case. However, there is a lack of papers that deal with the problem of outlier detection in symbolic data analysis. The paper aims to propose how to adapt isolation forests for symbolic data case. Methodology: Isolation forest for symbolic data is used to detect outliers in four different artificial datasets with known cluster structure and a known number of outliers. Results: The results show that the isolation forest for symbolic data is a fast and efficient tool for outlier mining. Implications and recommendations: As the isolation forest for symbolic data turned out to be an efficient tool for outlier detection for artificial data, further studies should focus on real data sets that contain outliers (i.e. credit card fraud dataset) and this approach should be compared with other outlier mining tools (i.e. DBCSAN). The authors recommend using the same initial settings for isolation forest for symbolic data as the settings that are proposed for isolation forest for classical data. Originality/value: This paper is the first of this kind, focusing not only on the problem of outlier detection in general, but it extends the well-known isolation forest model for symbolic data case.
Copyright (c) 2024 Marcin Pełka, Andrzej Dudek https://creativecommons.org/licenses/by-sa/4.0
Asymptotic Normality of Single Functional Index Quantile Regression for Functional Data with Missing Data at Random
This work addresses the problem of the nonparametric estimation of the regression function, namely the conditional distribution and the conditional quantile in the single functional index model (SFIM) under the independent and identically distributed condition with randomly missing data. The main result of this study was the establishment of the asymptotic properties of the estimator, such as the almost complete convergence rates. Moreover, the asymptotic normality of the constructs was obtained under certain mild conditions. Lastly, the authors discussed how to apply the result to construct confidence intervals.
Copyright (c) 2024 Anis Allal, Nadia Kadiri, Abbes Rabhi https://creativecommons.org/licenses/by-sa/4.0
Evaluation of the Labour Market Situation of Young People in EU Countries – The Multiple Regression Approach
The article refers to the topic of the SARS CoV-2 virus pandemic and focuses on the effect of vaccines against this virus. The relation between the administered vaccines and the development of the global pandemic is very pertinent as the problem is being faced by the whole world. The difficulty lies in the fight against the pandemic, which is the cause of the very high death rate due to the virus, and has caused a global economic crisis. Demonstrating patterns and possible anomalies between data on the number of people vaccinated and the course of the disease and the number of deaths is an important factor in raising awareness of the risk of spreading the virus. The methods presented in the second chapter are data agglomeration and the k-means method. The study compared the results obtained in six selected countries from different regions of the world and presented the most important factors influencing the development of the pandemic. The presented methodology was also the basis for a deeper discussion of the factors determining the spread of the virus and can be an introduction to the analysis of time series. At the same time, it enabled the creation of patterns related to the studied phenomenon (for selected countries) defining local factors contributing to the spread of the disease and determining the effectiveness of the vaccines administered in them. The empirical analysis was conducted on the basis of data available in the electronic scientific publication https://ourworldindata. org/. The visualisations were made in the Tableau program, and the cluster analysis was carried out using the Statistica package.
Copyright (c) 2023 Magdalena Kawecka https://creativecommons.org/licenses/by-sa/4.0
Single Functional Index Quantile Regression for Functional Data with Missing Data at Random
The primary goal of this research was to estimate the quantile of a conditional distribution using a semi-parametric approach in the presence of randomly missing data, where the predictor variable belongs to a semi-metric space. The authors assumed a single index structure to link the explanatory and response variable. First, a kernel estimator was proposed for the conditional distribution function, assuming that the data were selected from a stationary process with missing data at random (MAR). By imposing certain general conditions, the study established the model’s uniform almost complete consistencies with convergence rates.
Copyright (c) 2023 Nadia Kadiri, Sanaà Dounya Mekki, Abbes Rabhi https://creativecommons.org/licenses/by-sa/4.0
The Second Wave of the COVID-19 Pandemic in Poland - Characterised Using FDA Methods
The aim of this article was to analyse functional data of the number of hospitalised individuals, intensive care patients, positive COVID-19 tests, deaths and convalescents during the second wave of the COVID-19 pandemic in Poland. For this purpose, firstly the author convert data of sixteen voivodeships to smooth functions, and then used the principal component analysis and multiple function-on-function linear regression model to predict the number of hospitalised and intensive care patients due to the COVID-19 infection during the second wave of the pandemic. Finally, the results were compared with those previously obtained for the combined data of the second and third wave of the COVID-19 pandemic in Poland.
Copyright (c) 2023 Patrycja Hęćka https://creativecommons.org/licenses/by-sa/4.0
Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data
Aim: The aim of this article is to present and evaluate the concept of synthetic data. They are completely new, artificially generated data, but keep the statistical properties of real data. Due to the statistical similarity with real data, they can be used instead of them. This action allows data to be shared externally while guaranteeing their privacy. Methodology: New datasets were generated based on financial information about Polish limited liability companies, which come from the Orbis database and refers to 2020. To create synthetic data, it was decided to use generative models: CTGAN (based on GAN architecture) and TVAE (based on autoencoders). Finally, the synthetic data were compared with the real ones in terms of statistical properties (e.g. shape of distributions, correlations etc.) and their applicability to machine learning models (PCA method). Results: The Overall Quality Score was higher for the data generated by TVAE, but after examining the results in more detail, it was seen that the data generated by CTGAN had a better quality in terms of keeping the statistical properties of the real data. Comparing the results of the PCA method, TVAE was better than CTGAN. In addition, the TVAE method was less time-consuming than CTGAN. Implications and recommendations: Before publishing the synthetic data externally, it is recommended that the data are generated using several algorithms, evaluating their final results and finally selecting the best option. This action enables the resulting dataset to be of the highest quality. In further research, it is proposed that other algorithms are tested (e.g. CopulaGAN or TableGAN), in an attempt to deal with some of the realistic data problems that were missed in this analysis, such as missing values (the work was carried out with a complete dataset). Data generated in this study may be used to build financial indicators; which in turn could be used to construct company assessment models. Originality/value: Synthetic data help to deal with some of the data limitations, such as data privacy or scarcity. Due to their statistical similarity with real data, it is possible to use them in advanced machine learning methods instead of real datasets. Analysis on high quality synthetic data allows conclusions similar to analysis on real data to be achieved, while retaining privacy and without publishing sensitive data to third parties.
Copyright (c) 2024 Aleksandra Szymura https://creativecommons.org/licenses/by-sa/4.0
An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study
Aim: This study aimed to develop and apply the novel HexGIN (Heterogeneous extension for Graph Isomorphism Network) model to the FinCEN Files case data and compare its performance with existing solutions, such as the SAGE-based graph neural network and Multi-Layer Perceptron (MLP), to demonstrate its potential advantages in the field of anti-money laundering systems (AML). Methodology: The research employed the FinCEN Files case data to develop and apply the HexGIN model in a beneficiary prediction task for a suspicious transactions graph. The model's performance was compared with the existing solutions in a series of cross-validation experiments. Results: The experimental results on the cross-validation data and test dataset indicate the potential advantages of HexGIN over the existing solutions, such as MLP and Graph SAGE. The proposed model outperformed other algorithms in terms of F1 score, precision, and ROC AUC in both training and testing phases. Implications and recommendations: The findings demonstrate the potential of heterogeneous graph neural networks and their highly expressive architectures, such as GIN, in AML. Further research is needed, in particular to combine the proposed model with other existing algorithms and test the solution on various money-laundering datasets. Originality/value: Unlike many AML studies that rely on synthetic or undisclosed data sources, this research was based on a publicly available, real, heterogeneous transaction dataset, being part of a larger investigation. The results indicate a promising direction for the development of modern hybrid AML tools for analysing suspicious transactions; based on heterogeneous graph networks capable of handling various types of entities and their connections.
Copyright (c) 2024 Filip Wójcik https://creativecommons.org/licenses/by-sa/4.0
The Identification of Seasonality in the Housing Market Using the X13-ARIMA-SEATS Model
Aim: In the conducted research, profiles of seasonality in the housing market were determined, which provided an opportunity to answer two fundamental questions: what is the nature of harmonic variation in the seasonality and periodicity of the studied components of the construction process? what parameters of the ARIMA model optimally describe the construction market? Methodology: In the conducted research, using the X13-ARIMA-SEATS model, seasonal decomposition was carried out in the various stages of the housing construction process. Results: The research process conducted to identify seasonal fluctuations in the housing construction market showed that harmonic fluctuation profiles can be identified on an annual basis. An analysis of seasonal fluctuations was carried out for each of the three stages of the housing construction process, while also checking how these profiles function for Poland in general, and for individual investors, and for those building apartments for sale or to rent. The study showed that the market for real estate development activity differs in its seasonal characteristics from that of individual investors. Implications and recommendations: The conclusions obtained from the research can provide support in the decision-making process, both from a macro and microeconomic perspective. Parameterisation of the occurring fluctuations, and taking them into account in the process of developing a forecast can provide decision-making rationale in the implementation of macroprudential and financial stability policies. Originality/Value: A novelty is in the demonstration that the residential real estate market in Poland shows different seasonal parameters, divided into the market of individual investors and investors who build apartments for sale or rent.
Copyright (c) 2024 Łukasz Mach, Ireneusz Dąbrowski, Daria Wotzka, Paweł Frącz https://creativecommons.org/licenses/by-sa/4.0
The Unfolding Analysis for Symbolic Objects Based on the Example of the External Car Advertisement Evaluation
Aim: Multidimensional unfolding allows representing both columns (e.g. products, services) and rows (e.g. customers) of the preference matrix on the same low-dimensional map (usually it’s a two or three-dimensional map). The main aim of the paper was to propose how to perform unfolding analysis for symbolic objects. Methodology: The paper describes the possible ways of performing unfolding analysis for symbolic interval-valued data. The external unfolding is described in the details and used in the empirical part of the paper. The data (preferences and dissimilarities) were gathered by using the incomplete method of triads. Results: The empirical part presents an application for unfolding symbolic data to evaluate customers’ preferences, where car advertisements are used as the example. The results presented on a two-dimensional perceptual map allowed to discover seven groups of respondents with different preferences; most of them prefer Skoda, Audi, Volkswagen, and Honda advertisements to Toyota and Volvo. Implications and recommendations: The proposed external approach for symbolic data allows to represent objects as rectangles (on two-dimensional map) or cuboids (in the case of three dimensions). The respondents are represented as points. Further work should focus on creating an algorithm that allows for the presentation of both symbolic objects and preferences expressed by respondents in the form of rectangles or cuboids. Originality/Value: The paper presents an innovative and previously unpresented external unfolding for symbolic data. Besides that it presents how other unfolding approaches could be adapted for symbolic data.
Copyright (c) 2024 Artur Zaborski, Marcin Pełka https://creativecommons.org/licenses/by-sa/4.0
Cluster Analysis and Visualisation Describing the Phenomenon of the Covid-19 Virus Pandemic
Copyright (c) 2023 Grażyna Trzpiot, Zuzanna Krysiak https://creativecommons.org/licenses/by-sa/4.0
Remote Education during the Covid-19 Pandemic in the Opinion of Students
The Covid-19 pandemic forced an immediate switch from the traditional form of education to a remote one, which caused various problems. Comparative studies conducted among students and academic teachers have shown the complexity of the situation and the ambiguity of assessments concerning the effectiveness of the solutions applied. This article presents an analysis of the results of a survey carried out in Q4 of 2021, using own questionnaire, among students from universities in Poland, Hungary and Greece, and a group of attendees of the Adult Education Centre in Finland (N = 769). The presented area of research embraces students’ experiences of distance learning during the first stage of the Covid-19 pandemic. Descriptive statistics and tests of the equality of means were used to analyse the students’ opinions in order to check for differences in assessments based on the selected metric characteristics of the respondents. Possible differentiation of opinions due to respondent characteristics such as gender or country of study was also searched for. The study results can be used to develop solutions which can be used to improve the quality of distance learning.
Copyright (c) 2023 Dorota Kwiatkowska-Ciotucha, Urszula Załuska https://creativecommons.org/licenses/by-sa/4.0
Digitalization and the Information Society in Algeria: Digital Transformation Actors and Key Variables
The information society is a product of the intersection of socio-historical-technological contexts, where the development of science and industry has a global reach. The pervasiveness of new information and communication technologies makes it an essential component of a new civilization, affecting every country in varying degrees. Although this techniques approach is grounded in some truth, it neglects other vital aspects that challenge the idea of an information society that prioritizes human needs. The leading nations have achieved this through active research and development, driven by government involvement and the significant contributions of universities. Furthermore, the collaboration of diverse economic, institutional, social, and civic actors has played an essential role in its advancement. However, constructing and promoting an information society transcends infrastructure. It involves political actions that consider the socio-technological nature of this development and its impact on society and other sectors of activity. The objective of this article is twofold. On the one hand, the study analysed the situation of Algeria and its position in relation to other countries regarding the information society, and the other examined the factors that influence the development of the information society in Algeria, trying to identify the most important ones. Finally, the authors proposed a development strategy in this area. The research thesis was formulated as follows: What are the key variables that have an impact on the development of digitalization and the information society in Algeria? Thus, the regulatory frameworks of the most advanced countries are central to the initiatives aimed at its development. To foster the emergence of an information society in Algeria, solidarity must be strengthened, diversity promoted, and the potential of all citizens catalysed.
information and communication technologies
Copyright (c) 2023 Maria Bouberka, Sabah Fadel, Hassan Derrar https://creativecommons.org/licenses/by-sa/4.0
Regional Volatility Linkages: Impact of Neighbouring Currencies on Nigeria's Currency Instability
This study examines the effect of the exchange rate volatility of currencies of countries bordering Nigeria, namely the Benin Republic, Niger, Chad and the Cameroon Republic on Nigeria's exchange rate volatility using monthly observations for 1st January 2001 to 31st December 2021. The study employed the Generalised Auto-Regressive Conditional Heteroscedasticity method to analyse the dataset. The study found that fluctuations in the currencies of these countries have a significant impact on the volatility of Nigeria's currency naira. As a result, it is recommended that policymakers and government agencies strengthen security along the Nigeria-Cameroon, Nigeria-Benin, Nigeria-Chad, and Nigeria-Niger borders to more strictly regulate imports and support the stability of Nigeria's currency. The Nigerian government should encourage less importation from these countries through import-substitute production and higher exportation from Nigeria as such could lead to the appreciation of the naira.
Copyright (c) 2024 Abdurrauf Babalola, Kudu Ibn Muhammad https://creativecommons.org/licenses/by-sa/4.0
The Prospect Theory and First Price Auctions: an Explanation of Overbidding
This paper attempted using the prospect theory to explain overbidding in first price auctions. The standard outlook in the literature on auctions is that bidders overbid, but the probability weighting functions are nonlinear as in the prospect theory, so they not only tend to underweight the probabilities of winning the auction but also overweight, so that there are overbidders and underbidders. This paper proves that to some extent, non-linear weighting functions do explain overbidding the risk-neutral Nash equilibrium valuation (RNNE). Furthermore, coherent risk measures, such as certainty equivalent and translation invariance, were used to show loss aversion among bidders, and in line with the prospect theory, convexity was also confirmed with sub-additivity, monotonicity and with positive homogeneity.
Copyright (c) 2023 Dushko Josheski, Mico Apostolov https://creativecommons.org/licenses/by-sa/4.0
The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random
The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.
Copyright (c) 2023 Wahiba Bouabsa https://creativecommons.org/licenses/by-sa/4.0
The Application of Association Rules to Detect the Effects of Vaccinations against COVID-19 in the EU-27. Preliminary estimates
In this research study, the authors obtained the preliminary evaluation of the impact detection of vaccinations against COVID-19 in the EU-27. The empirical basis of the study was the daily number of COVID-19 cases, vaccinations, hospitalisations, and deaths in the EU countries from March 2020 to March 2022. Rules of association were used to identify non-obvious associations between vaccinations against COVID-19 and cases of illness, hospitalisations, and deaths from COVID-19. The obtained results were used to cluster the EU countries by the level of vaccinations against COVID-19, cases of COVID-19, deaths from COVID, and COVID-19 hospitalisations for the EU member states. The K-means clustering method was used for cluster analysis. Hidden dependencies of the number of COVID-19 cases, the number of COVID-19 hospitalisations, and the number of COVID-19 deaths due to the number of vaccinations against COVID-19 by EU countries were revealed. It was established with a high probability that vaccination significantly affects the level of morbidity. For the first time, association rules were obtained, which are preliminary estimates of the relationship between the dynamics of vaccinations against COVID-19 and the dynamics of COVID-19 cases, COVID-19 hospitalisations, and deaths from COVID-19 in the EU. The results can be used to make beneficial decisions, for example, to regulate vaccination policies in individual EU countries, and predict the future consequences of the COVID-19 pandemic.
Copyright (c) 2023 Kateryna Berezka, Olha Kovalchuk https://creativecommons.org/licenses/by-sa/4.0