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Econometrics, Volume 13, Issue 1 (March 2025) – 5 articles

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12 pages, 611 KiB  
Article
Application of Fuzzy Discount Factors in Behavioural Decision-Making for Financial Market Modelling
by Joanna Siwek and Patryk Żywica
Econometrics 2025, 13(1), 5; https://doi.org/10.3390/econometrics13010005 - 26 Jan 2025
Viewed by 374
Abstract
This paper presents an innovative approach to financial market modelling by integrating fuzzy discount factors into the decision-making process, thereby reflecting the complexities of human behaviour. Traditional financial models often fail to account for market dynamics’ psychological factors. The proposed method utilizes fuzzy [...] Read more.
This paper presents an innovative approach to financial market modelling by integrating fuzzy discount factors into the decision-making process, thereby reflecting the complexities of human behaviour. Traditional financial models often fail to account for market dynamics’ psychological factors. The proposed method utilizes fuzzy logic to encapsulate the uncertainty and subjective judgment inherent in financial decisions. By representing financial variables as fuzzy numbers, the model better simulates the way humans assess information and make decisions under uncertainty. The incorporation of fuzzy discount factors marks a significant shift from deterministic to a more realistic representation of financial markets, suitable for practical application. This methodology offers a nuanced investment strategy that balances theoretical rigour with real-world applicability, appealing to a broad spectrum of investors. The aim of the following paper is to introduce an alternative to price modelling with the use of fuzzy return rates, which results in some errors in the mathematical model. The solution has the form of introducing fuzzy discount factors (FDFs) that retain the advantages of the fuzzy approach (e.g., encompassing subjectivity and imprecision) while preserving the shape of the fuzzy number modelling a price. Full article
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24 pages, 337 KiB  
Article
An Economic Theory with a Formal-Econometric Test of Its Empirical Relevance
by Bernt Petter Stigum
Econometrics 2025, 13(1), 4; https://doi.org/10.3390/econometrics13010004 - 16 Jan 2025
Viewed by 482
Abstract
The paper contains five parts—a theory about entrepreneurial choice under uncertainty, a formal econometric structure for a test, the test, an appraisal of the test, and a description of the data generating process. Here, an entrepreneur is an individual who manages a firm [...] Read more.
The paper contains five parts—a theory about entrepreneurial choice under uncertainty, a formal econometric structure for a test, the test, an appraisal of the test, and a description of the data generating process. Here, an entrepreneur is an individual who manages a firm that produces one commodity with labor, an intermediate good, and capital. He pays dividends to shareholders, invests in bonds and capital, and has an n-period planning horizon. Conditioned on the values of current-period prices, the entrepreneur aims to maximize the expected value of a utility function that varies with the dividends he pays each period and with his firm’s balance sheet variables at the end of the planning horizon. The test comprises a family of trials of theorems that I derive from the axioms of the theory part of the formal econometric structure. In the test, the theorems are appraised for their empirical relevance in an empirical context, where each one of a random sample of four hundred entrepreneurs has chosen the first-period part of his optimal n-period expenditure plan. My formal econometric arguments demonstrate that the theorems pass all the trials. At the end, I show that my formal econometric results imply that the theory is empirically relevant. Full article
23 pages, 827 KiB  
Article
Optimal Time Series Forecasting Through the GARMA Model
by Adel Hassan A. Gadhi, Shelton Peiris, David E. Allen and Richard Hunt
Econometrics 2025, 13(1), 3; https://doi.org/10.3390/econometrics13010003 - 8 Jan 2025
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Abstract
This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting long-memory time [...] Read more.
This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting long-memory time series. This characteristic is confirmed using popular datasets such as sunspot data and Australian beer production data. This approach provides a robust framework for accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other implemented models, such as GARMA, TBATS, ARIMA, and ANN, highlighting its ability to address both long-memory and non-linear dynamics. Finally, we discuss the representativeness of the datasets selected and the adaptability of the proposed hybrid model to various time series scenarios. Full article
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26 pages, 850 KiB  
Article
Forecasting Half-Hourly Electricity Prices Using a Mixed-Frequency Structural VAR Framework
by Gaurav Kapoor, Nuttanan Wichitaksorn, Mengheng Li and Wenjun Zhang
Econometrics 2025, 13(1), 2; https://doi.org/10.3390/econometrics13010002 - 8 Jan 2025
Viewed by 475
Abstract
Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this [...] Read more.
Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this paper, we employ a mixed-frequency vector autoregression (MF-VAR) framework where we propose a VAR specification to the reverse unrestricted mixed-data sampling (RU-MIDAS) model, called RU-MIDAS-VAR, to provide point forecasts of half-hourly electricity prices using several weather variables and electricity demand. A key focus of this study is the use of variational Bayes as an estimation technique and its comparison with other well-known Bayesian estimation methods. We separate forecasts for peak and off-peak periods in a day since we are primarily concerned with forecasts for peak periods. Our forecasts, which include peak and off-peak data, show that weather variables and demand as regressors can replicate some key characteristics of electricity prices. We also find the MF-VAR and RU-MIDAS-VAR models achieve similar forecast results. Using the LASSO, adaptive LASSO, and random subspace regression as dimension-reduction and variable selection methods helps to improve forecasts where random subspace methods perform well for large parameter sets while the LASSO significantly improves our forecasting results in all scenarios. Full article
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16 pages, 1296 KiB  
Article
Relationship Between Coefficients in Parametric Survival Models for Exponentially Distributed Survival Time—Registered Unemployment in Poland
by Beata Bieszk-Stolorz
Econometrics 2025, 13(1), 1; https://doi.org/10.3390/econometrics13010001 - 2 Jan 2025
Viewed by 571
Abstract
Survival analysis is a popular research tool in medicine and demography. It has been used for many years to study the duration of socio-economic phenomena. The aim of this article is to evaluate the relationship between the coefficients of the proportional hazards model [...] Read more.
Survival analysis is a popular research tool in medicine and demography. It has been used for many years to study the duration of socio-economic phenomena. The aim of this article is to evaluate the relationship between the coefficients of the proportional hazards model (PH) and the accelerated failure time model (AFT), assuming an exponential distribution of survival time. The coefficients of the PH and AFT exponential models have the same magnitude but have opposite signs. It follows that there is a symmetric relation between the coefficients. In the case of exponential PH and AFT models, there is a relation of equality between the parameters describing the quality and fit of the model, as well as between the standard errors of the parameters of both models. In this case also, we can talk about a symmetric relation. The exponential PH model is valid if the exponential AFT model is valid. The study showed that the intensity of starting work was higher in the case of men, people with work experience, people with higher education and young people. The job search time was longer for women, people with no work experience, and people aged 60+, but shorter for people with higher education. Full article
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