Econometrics

15 pages, 1726 KiB

Open AccessArticle

Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques

by Giovanni Masala and Amelie Schischke

Econometrics 2024, 12(4), 34; https://doi.org/10.3390/econometrics12040034 - 12 Nov 2024

Viewed by 476

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices [...] Read more.

Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plant located in Italy and used the relevant climate series to determine the quantity of energy produced. We forecast the quantity of energy as well as income through machine learning techniques and more traditional statistical and econometric models. We evaluate the results by splitting the dataset into estimation windows and test windows, and using a backtesting technique. In particular, we found evidence that ML regression techniques outperform results obtained with traditional econometric models. Regarding the models used to achieve this goal, the objective is not to propose original models but to verify the effectiveness of the most recent machine learning models for this important application, and to compare them with more classic linear regression techniques. Full article

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11 pages, 227 KiB

Open AccessArticle

Likert Scale Variables in Personal Finance Research: The Neutral Category Problem

by Blain Pearson, Donald Lacombe and Nasima Khatun

Econometrics 2024, 12(4), 33; https://doi.org/10.3390/econometrics12040033 - 6 Nov 2024

Viewed by 544

Abstract

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical [...] Read more.

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the “neutral” category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights. Full article

19 pages, 579 KiB

Open AccessArticle

Enhancing Efficiency: Halton Draws in the Generalized True Random Effects Model

by David H. Bernstein

Econometrics 2024, 12(4), 32; https://doi.org/10.3390/econometrics12040032 - 6 Nov 2024

Viewed by 422

Abstract

This paper measures the impact of the number of Halton draws in excess of

⌈ \sqrt{n} ⌉

on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates [...] Read more.

This paper measures the impact of the number of Halton draws in excess of

⌈ \sqrt{n} ⌉

on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to

⌈ n^{3 / 4} ⌉

(

⌈ n^{2 / 3} ⌉

) decreases the mean squared error of the total technical efficiency estimates by

6.1

(

4.9

) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work. Full article

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19 pages, 909 KiB

Open AccessArticle

Exploring the Role of Global Value Chain Position in Economic Models for Bankruptcy Forecasting

by Mélanie Croquet, Loredana Cultrera, Dimitri Laroutis, Laetitia Pozniak and Guillaume Vermeylen

Econometrics 2024, 12(4), 31; https://doi.org/10.3390/econometrics12040031 - 5 Nov 2024

Viewed by 471

Abstract

This study addresses a significant gap in the literature by comparing the effectiveness of traditional statistical methods with artificial intelligence (AI) techniques in predicting bankruptcy among small and medium-sized enterprises (SMEs). Traditional bankruptcy prediction models often fail to account for the unique characteristics [...] Read more.

This study addresses a significant gap in the literature by comparing the effectiveness of traditional statistical methods with artificial intelligence (AI) techniques in predicting bankruptcy among small and medium-sized enterprises (SMEs). Traditional bankruptcy prediction models often fail to account for the unique characteristics of SMEs, such as their vulnerability due to lean structures and reliance on short-term credit. This research utilizes a comprehensive database of 7104 Belgian SMEs to evaluate these models. Belgium was selected due to its unique regulatory and economic environment, which presents specific challenges and opportunities for bankruptcy prediction in SMEs. Our findings reveal that AI techniques significantly outperform traditional statistical methods in predicting bankruptcy, demonstrating superior predictive accuracy. Furthermore, our analysis highlights that a firm’s position within the Global Value Chain (GVC) impacts prediction accuracy. Specifically, firms operating upstream in the production process show lower prediction performance, suggesting that bankruptcy risk may propagate upward along the value chain. This effect was measured by analyzing the firm’s GVC position as a variable in the prediction models, with upstream firms exhibiting greater vulnerability to the financial distress of downstream partners. These insights are valuable for practitioners, emphasizing the need to consider specific performance factors based on the firm’s position within the GVC when assessing bankruptcy risk. By integrating both AI techniques and GVC positioning into bankruptcy prediction models, this study provides a more nuanced understanding of bankruptcy risks for SMEs and offers practical guidance for managing and mitigating these risks. Full article

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19 pages, 3084 KiB

Open AccessArticle

Impact of Areal Factors on Students’ Travel Mode Choices: A Bayesian Spatial Analysis

by Amin Azimian and Alireza Azimian

Econometrics 2024, 12(4), 30; https://doi.org/10.3390/econometrics12040030 - 26 Oct 2024

Viewed by 576

Abstract

A preliminary analysis of the 2018/2019 Austin Travel Survey indicated that most off-campus students in Travis County, TX, tend to use cars rather than more sustainable transportation modes, significantly contributing to traffic congestion and environmental impact. This study aims to analyze the impacts [...] Read more.

A preliminary analysis of the 2018/2019 Austin Travel Survey indicated that most off-campus students in Travis County, TX, tend to use cars rather than more sustainable transportation modes, significantly contributing to traffic congestion and environmental impact. This study aims to analyze the impacts of areal factors, including environmental and transportation factors, on students’ choices of travel mode in order to promote more sustainable transport behaviors. Additionally, we investigate the presence of spatial correlation and unobserved heterogeneity in travel data and their effects on students’ travel mode choices. We have proposed two Bayesian models—a basic model and a spatial model—with structured and unstructured random-effect terms to perform the analysis. The results indicate that the inclusion of spatial random effects considerably improves model performance, suggesting that students’ choices of mode are likely influenced by areal factors often ‘unobserved’ in many individual travel mode choice surveys. Furthermore, we found that the average slope, sidewalk density, and bus-stop density significantly affect students’ travel mode choices. These findings provide insights into promoting sustainable transport systems by addressing environmental and infrastructural factors in an effort to reduce car dependency among students, thereby supporting sustainable urban development. Full article

(This article belongs to the Special Issue Innovations in Bayesian Econometrics: Theory, Techniques, and Economic Analysis)

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26 pages, 1471 KiB

Open AccessArticle

Econometric Analysis of the Sustainability and Development of an Alternative Strategy to Gross Value Added in Kazakhstan’s Agricultural Sector

by Azat Tleubayev, Seyit Kerimkhulle, Manatzhan Tleuzhanova, Aigul Uchkampirova, Zhanat Bulakbay, Raikhan Mugauina, Zhumagul Tazhibayeva, Alibek Adalbek, Yerassyl Iskakov and Daniyar Toleubay

Econometrics 2024, 12(4), 29; https://doi.org/10.3390/econometrics12040029 - 17 Oct 2024

Viewed by 1421

Abstract

Based on the systematization of relevant problems in the agricultural sector of Kazakhstan and other countries, the purpose of the research is to aid in the development and implementation of a methodology for the econometric analysis of sustainability, the classification of economic growth, [...] Read more.

Based on the systematization of relevant problems in the agricultural sector of Kazakhstan and other countries, the purpose of the research is to aid in the development and implementation of a methodology for the econometric analysis of sustainability, the classification of economic growth, and an alternative strategy for gross value added depending on time phases with time lags of 0, 1, and 2 years, and on the gross fixed capital formation in the agricultural sector of Kazakhstan. The research has used a variety of quantitative techniques, including the logistic growth difference equation, applied statistics, econometric models, operations research, nonlinear mathematical programming models, economic modeling simulations, and sustainability analysis. In the work on three criteria: equilibrium, balanced and optimal growth, we have defined the main trends of growth of Gross added value of agriculture, hunting and forestry. The first, depending on the time phases, the second, depending on the Gross fixed capital formation transactions for equilibrium growth, for the growth of an alternative strategy, for the endogenous growth rate and the growth of exogenous flows. And we also received a classification of the trend of Productive, Moderate and Critical growth for the agricultural industry depending on the correlated linkaged industry of the national economy of Kazakhstan. The results of this work can be used in data analytics and artificial intelligence, digital transformation and technology in agriculture, as well as in the areas of sustainability and environmental impact. Full article

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20 pages, 478 KiB

Open AccessArticle

Long-Term Care in Germany in the Context of the Demographic Transition—An Outlook for the Expenses of Long-Term Care Insurance through 2050

by Patrizio Vanella, Christina Benita Wilke and Moritz Heß

Econometrics 2024, 12(4), 28; https://doi.org/10.3390/econometrics12040028 - 9 Oct 2024

Viewed by 806

Abstract

Demographic aging results in a growing number of older people in need of care in many regions all over the world. Germany has witnessed steady population aging for decades, prompting policymakers and other stakeholders to discuss how to fulfill the rapidly growing demand [...] Read more.

Demographic aging results in a growing number of older people in need of care in many regions all over the world. Germany has witnessed steady population aging for decades, prompting policymakers and other stakeholders to discuss how to fulfill the rapidly growing demand for care workers and finance the rising costs of long-term care. Informed decisions on this matter to ensure the sustainability of the statutory long-term care insurance system require reliable knowledge of the associated future costs. These need to be simulated based on well-designed forecast models that holistically include the complexity of the forecast problem, namely the demographic transition, epidemiological trends, concrete demand for and supply of specific care services, and the respective costs. Care risks heavily depend on demographics, both in absolute terms and according to severity. The number of persons in need of care, disaggregated by severity of disability, in turn, is the main driver of the remuneration that is paid by long-term care insurance. Therefore, detailed forecasts of the population and care rates are important ingredients for forecasts of long-term care insurance expenditures. We present a novel approach based on a stochastic demographic cohort-component approach that includes trends in age- and sex-specific care rates and the demand for specific care services, given changing preferences over the life course. The model is executed for Germany until the year 2050 as a case study. Full article

(This article belongs to the Special Issue Advancements in Macroeconometric Modeling and Time Series Analysis)

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17 pages, 299 KiB

Open AccessArticle

Estimating the Effects of Credit Constraints on Productivity of Peruvian Agriculture

by Tiemen Woutersen, Katherine Hauck and Shahidur R. Khandker

Econometrics 2024, 12(4), 27; https://doi.org/10.3390/econometrics12040027 - 26 Sep 2024

Viewed by 632

Abstract

This paper proposes an estimator for the endogenous switching regression models with fixed effects. The decision to switch from one regime to the other may depend on unobserved factors, which would cause the state, such as being credit constrained, to be endogenous. Our [...] Read more.

This paper proposes an estimator for the endogenous switching regression models with fixed effects. The decision to switch from one regime to the other may depend on unobserved factors, which would cause the state, such as being credit constrained, to be endogenous. Our estimator allows for this endogenous selection and for conditional heteroscedasticity in the outcome equation. Applying our estimator to a dataset on the productivity in agriculture substantially changes the conclusions compared to earlier analysis of the same dataset. Intuitively, the reason that our estimate of the impact of switching between states is smaller than previously estimated is that we captured the selection issue: switching between being credit constrained and credit unconstrained may be endogenous to farm production. In particular, we find that being credit constant has the substantial effect of reducing yield by 11%, but not the previously estimated very dramatic effect of reducing yield by 26%. Full article

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Econometrics, Volume 12, Issue 4 (December 2024) – 8 articles

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