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Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition
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Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11798

Special Issue Editors

Prof. Dr. Anna Firsova

E-Mail Website
Guest Editor
Faculty of Economics, Saratov State University, 410012 Saratov, Russia
Interests: data envelopment analysis; quantitative analysis; innovations; R&D, regional development; finance
Special Issues, Collections and Topics in MDPI journals
Dr. Galina Chernyshova

E-Mail Website
Guest Editor
Faculty of Computer Science and Information Technologies, Saratov State University, 410012 Saratov, Russia
Interests: mathematical modeling in economy; data mining; data envelopment analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantitative analysis is an important tool for assessing the efficiency of economic processes. Mathematical modeling based on data envelopment analysis provides the measurement of relative efficiency of decision-making units. These approaches allow increasing the objectivity and scientific validity of decision making in applied economics.

This Special Issue aims to contribute to the theory, methodology, analysis, applications, and strategies of modern evaluation approaches that may bring novel insights into quantitative analysis methods in the economy.

Original theoretical and empirical articles containing analysis and interpretation of quantitative techniques for a wide range of problems in applied economics are accepted. Topics of interest include but are not limited to mathematical models used in business, finance, agriculture, education, energy industry, transport, culture, healthcare, regional and spatial development, public administration, and others.

Prof. Dr. Anna Firsova
Dr. Galina Chernyshova
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantitative analysis
  • decision making methods
  • data analysis
  • data envelopment analysis
  • optimization models
  • statistical analysis
  • econometrics modelling
  • data mining
  • performance measurement
  • making predictions

Published Papers (7 papers)

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Research

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26 pages, 797 KiB  
Communication
Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model
by Aleksandar Kemiveš, Lidija Barjaktarović, Milan Ranđelović, Milan Čabarkapa and Dragan Ranđelović
Mathematics 2024, 12(7), 1020; https://doi.org/10.3390/math12071020 - 28 Mar 2024
Viewed by 427
Abstract
Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the [...] Read more.
Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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21 pages, 367 KiB  
Article
Two-Stage Data Envelopment Analysis Models with Negative System Outputs for the Efficiency Evaluation of Government Financial Policies
by Andrey V. Lychev, Svetlana V. Ratner and Vladimir E. Krivonozhko
Mathematics 2023, 11(24), 4873; https://doi.org/10.3390/math11244873 - 5 Dec 2023
Cited by 1 | Viewed by 939
Abstract
The main purpose of this study is to provide a comparative analysis of several possible approaches to applying data envelopment analysis (DEA) in the case where some decision making units (DMUs) in the original sample have negative system outputs. In comparison to the [...] Read more.
The main purpose of this study is to provide a comparative analysis of several possible approaches to applying data envelopment analysis (DEA) in the case where some decision making units (DMUs) in the original sample have negative system outputs. In comparison to the traditional model of Charnes, Cooper, and Rhodes (CCR) and the CCR model with a scale shift to measure second-stage outputs, the range directional measure (RDM) model produces the most appropriate results. In this paper, an approach is proposed for estimating returns to scale. The study applies a two-stage DEA model with negative second-stage outputs to assess the public support for research, development, and demonstration projects in the energy sector in 23 countries over the period from 2010 to 2018. The assessment of government performance depends on its contribution to the growth of energy efficiency in the national economy and the reduction of its carbon intensity. Intermediate outputs (patents in the energy sector) are included in the analysis as both outputs of the first stage and inputs of the second stage. Taking the similarity between the calculations obtained without stage separation and the system efficiency calculations from the two-stage model as a measure of model adequacy, the RDM model shows the highest similarity scores. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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22 pages, 8990 KiB  
Article
Simulation Cognitive Modeling Approach to the Regional Sustainable Complex System Development for Improving Quality of Life
by Anna Firsova, Galina Gorelova, Elena L. Makarova, Elena A. Makarova and Galina Chernyshova
Mathematics 2023, 11(20), 4369; https://doi.org/10.3390/math11204369 - 20 Oct 2023
Cited by 1 | Viewed by 727
Abstract
This article presents study results of the region’s sustainable development possibility, thus improving the population’s life quality using cognitive simulation methods of complex systems. The main theoretical provisions of cognitive modeling developed and tested earlier in various socioeconomic system modeling are briefly outlined. [...] Read more.
This article presents study results of the region’s sustainable development possibility, thus improving the population’s life quality using cognitive simulation methods of complex systems. The main theoretical provisions of cognitive modeling developed and tested earlier in various socioeconomic system modeling are briefly outlined. The cognitive modeling application’s mathematical apparatus and CMCS software No. 2018661506 system were developed using quantitative data from one of Russia’s southern regions (Rostov oblast). The task was to study the region, model, understand, explain, and develop possible situation development scenarios, and foresee this complex system’s possible future outcome. The main statistical socioeconomic indicators of the state region were studied and processed. Data analysis necessary for developing and researching a cognitive model is given. The regional economic mechanism cognitive model is a functional graph consisting of quantitative and qualitative concepts. Between them, relationships are given in the form of functions, which is the novelty of research. The results of several scenarios of impulse modeling are presented, making it possible to predict future desirable and undesirable processes in the system. Scenario analysis was carried out, making it possible to propose a number of recommendations for the region’s sustainable development. A direction for the region’s development of further cognitive research is proposed. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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19 pages, 2553 KiB  
Article
Methodology for Assessing the Risks of Regional Competitiveness Based on the Kolmogorov–Chapman Equations
by Galina Chernyshova, Irina Veshneva, Anna Firsova, Elena L. Makarova and Elena A. Makarova
Mathematics 2023, 11(19), 4206; https://doi.org/10.3390/math11194206 - 9 Oct 2023
Viewed by 686
Abstract
The relevance of research on competitiveness at the meso level is related to the contemporary views of a region as an essential element of the economic space. The development of forecasting and analytical methods at the regional level of the economy is a [...] Read more.
The relevance of research on competitiveness at the meso level is related to the contemporary views of a region as an essential element of the economic space. The development of forecasting and analytical methods at the regional level of the economy is a key task in the process of strategic decision making. This article proposes a method of quantitative assessment of the risks of regional competitiveness. The novelty of this approach is based on both a fixed-point risk assessment and scenario-based predictive analysis. A hierarchical structure of indicators of competitiveness of regions is offered. A method based on the Kolmogorov–Chapman equations was used for the predictive estimation of risks of regional competitiveness. The integrated risk assessment is performed using the modified fuzzy ELECTRE II method. A web application has been implemented to assess the risks of competitiveness of Russian regions. The functionality of this application provides the use of multi-criteria decision-making methods based on a fuzzy logic approach to estimate risks at a specified time, calculating the probability of risk events and their combinations in the following periods and visualizing the results. Approbation of the technique was carried out for 78 Russian regions for various scenarios. The analysis of the results obtained provides an opportunity to identify the riskiest factors of regional competitiveness and to distinguish regions with different risk levels. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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18 pages, 503 KiB  
Article
Influences of Talent Cultivation and Utilization on the National Human Resource Development System Performance: An International Study Using a Two-Stage Data Envelopment Analysis Model
by Chia-Chin Chang and Chia-Syuan Chang
Mathematics 2023, 11(13), 2824; https://doi.org/10.3390/math11132824 - 23 Jun 2023
Cited by 1 | Viewed by 1068
Abstract
To enhance national competitiveness, countries are committed to building a National Human Resource Development (NHRD) system to develop talents. However, studies have rarely investigated the internal development process of the NHRD system and the performance at the system and sub-system levels. Thus, this [...] Read more.
To enhance national competitiveness, countries are committed to building a National Human Resource Development (NHRD) system to develop talents. However, studies have rarely investigated the internal development process of the NHRD system and the performance at the system and sub-system levels. Thus, this study constructed the performance evaluation model of the NHRD system from a two-stage process efficiency perspective that first cultivates talents and then uses the talents produced to create value. In addition, considering the problem of international talent flow and the time-lag effect, the bad output and the time-lag between inputs and outputs were incorporated into the model. The subjects included 60 countries, including Argentina, China, and OECD member countries. The results reveal that countries that excel at nurturing talents do not necessarily have the ability to effectively use talents to create value. Only having high-efficiency talent cultivation cannot strengthen competitiveness. Sensitivity analysis was also conducted to identify the input that affects talent cultivation and utilization efficiency, which could be used as a reference for competitiveness and NHRD performance improvement. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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30 pages, 955 KiB  
Article
How Does the Competitiveness Index Promote Foreign Direct Investment at the Provincial Level in Vietnam? An Integrated Grey Delphi–DEA Model Approach
by Phi-Hung Nguyen, Thi-Ly Nguyen, Hong-Quan Le, Thuy-Quynh Pham, Hoang-Anh Nguyen and Chi-Vinh Pham
Mathematics 2023, 11(6), 1500; https://doi.org/10.3390/math11061500 - 19 Mar 2023
Cited by 6 | Viewed by 4445
Abstract
Foreign direct investment (FDI) is an important factor in building a strong economy for a country, particularly in developing and emerging markets. Both domestic enterprises and policy makers have been motivated to attract FDI for the benefits of FDI, such as technological transfers, [...] Read more.
Foreign direct investment (FDI) is an important factor in building a strong economy for a country, particularly in developing and emerging markets. Both domestic enterprises and policy makers have been motivated to attract FDI for the benefits of FDI, such as technological transfers, spillover benefits, and rising competition. There is a need for a functional model to assess how the competitive index affects FDI attractiveness. Therefore, in this study, the authors use an integrated model of Grey Delphi, the Data Envelopment Analysis Super Slack-Based Measure Model (DEA–Super SBM), and the Malmquist Model (DEA–Malmquist) to evaluate the FDI attractiveness of Vietnamese provinces from 2017 to 2021. Firstly, ten critical dimensions of the provincial competitive index (PCI) affecting the number of FDI by cases and amount of FDI capital were validated via the Grey Delphi method. Secondly, the Super-SBM model is applied to assess the FDI efficiency of 63 provinces in Vietnam from 2017 to 2021. Then, the DEA–Malmquist model is employed to analyze the total change in the productivity of 63 provinces’ FDI performance in Vietnam. The findings of this study revealed that the efficiency of FDI in Vietnam’s provinces is relatively low, and there is a significant variation in the attractiveness of FDI among the provinces. This study can provide valuable insights for policy makers and other stakeholders in developing effective strategies to attract FDI and foster economic development. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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Review

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24 pages, 5111 KiB  
Review
Network DEA and Its Applications (2017–2022): A Systematic Literature Review
by Svetlana V. Ratner, Artem M. Shaposhnikov and Andrey V. Lychev
Mathematics 2023, 11(9), 2141; https://doi.org/10.3390/math11092141 - 3 May 2023
Cited by 11 | Viewed by 2669
Abstract
Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can [...] Read more.
Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them. Full article
(This article belongs to the Special Issue Quantitative Analysis and DEA Modeling in Applied Economics, 2nd Edition)
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