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Mathematics
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Advances in Theoretical and Empirical Economic Modeling
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Advances in Theoretical and Empirical Economic Modeling

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1896

Special Issue Editors

Prof. Dr. Hany Guirguis

E-Mail Website
Guest Editor
The O’Malley School of Business, Manhattan College, Riverdale, NY 10471, USA
Interests: financial modeling; monetary policy; econometrics
Dr. Hyeon Park

E-Mail Website
Guest Editor
The O’Malley School of Business, Manhattan College, Riverdale, NY 10471, USA
Interests: intertemporal choice; bounded rationality; mathematical economics

Special Issue Information

Dear Colleagues,

In recent years, significant mathematical developments have been made in empirical and theoretical economic modeling in response to the growing complexity of the American and international economies. Economic models now commonly incorporate nonlinearity, asymmetry, globalization, and structural shifts. To tackle these challenges, advanced estimation techniques such as artificial intelligence (AI), time-varying parameters (TVP), Markov Chain Monte Carlo (MCMC), and factor analysis have gained considerable attention. In terms of theoretical economic modeling, there have been noteworthy advancements, including relaxation in household rationality on consumption, saving, and portfolio choice; analysis of behavioral and information frictions on general equilibrium; transmission of shocks in monetary and fiscal policy under uncertainty; implications of asset valuation on monetary policy; economic perspectives on digital currency; and general equilibrium with labor-saving technology by artificial intelligence.

We welcome submissions of mathematical and quantitative research works that have the potential to significantly advance the field of economic modeling. Specifically, we encourage submissions in the following areas:

  1. Econometrics, macro-metrics, financial econometrics;
  2. Quantitative economics;
  3. Mathematical economics;
  4. Mathematical modeling in economics and finance;
  5. Optimization techniques;
  6. Stochastic optimization.

Prof. Dr. Hany Guirguis
Dr. Hyeon Park
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

  • econometrics
  • quantitative economics
  • mathematical economics
  • mathematical modeling in economics and finance
  • optimization techniques

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

33 pages, 3187 KiB  
Article
Predicting Firm’s Performance Based on Panel Data: Using Hybrid Methods to Improve Forecast Accuracy
by Nikita V. Martyushev, Vladislav Spitsin, Roman V. Klyuev, Lubov Spitsina, Vladimir Yu. Konyukhov, Tatiana A. Oparina and Aleksandr E. Boltrushevich
Mathematics 2025, 13(8), 1247; https://doi.org/10.3390/math13081247 - 10 Apr 2025
Viewed by 663
Abstract
The problem of predicting profitability is exceptionally relevant for investors and company owners making decisions about investment and business development. The global literature contains a number of studies where researchers predict the profitability of firms using various methods, including modern machine learning. However, [...] Read more.
The problem of predicting profitability is exceptionally relevant for investors and company owners making decisions about investment and business development. The global literature contains a number of studies where researchers predict the profitability of firms using various methods, including modern machine learning. However, these works hardly take advantage of panel data. This paper takes advantage of additional capabilities offered by panel data and proposes hybrid forecasting methods based on panel data, which allow significantly improving the accuracy of predicting the profitability. Our calculations show that when predicting the profitability, investors and company owners should take into account the profitability of the previous years and the trend in its change. The work shows that this approach can be successfully applied to high-tech companies whose profitability is characterised by increased volatility. Prediction forecasting includes STL-decomposition of time series, regression with random effects and machine learning (LSTM and CatBoost), and clustering. The training sample includes 1811 companies and data for 2013–2018 (panel data, 10,866 observations). The test sample contains data for these companies for 2019. As a result, the authors propose an approach significantly improving the accuracy of predicting ROA and ROE based on the panel nature of the data. The panel data allowed using the profitability of the previous years in forecast models and applying the STL-decomposition of the profitability of the previous years into three variables (Trend, Seasonal, and Residual), considerably improving the quality of the constructed forecast models (STL-CatBoost, STL-LSTM, and STL-RE hybrid models). Full article
(This article belongs to the Special Issue Advances in Theoretical and Empirical Economic Modeling)
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18 pages, 287 KiB  
Article
Dynamic Optimization with Timing Risk
by Erin Cottle Hunt and Frank N. Caliendo
Mathematics 2024, 12(17), 2654; https://doi.org/10.3390/math12172654 - 27 Aug 2024
Viewed by 715
Abstract
Timing risk refers to a situation in which the timing of an economically important event is unknown (risky) from the perspective of an economic decision maker. While this special class of dynamic stochastic control problems has many applications in economics, the methods used [...] Read more.
Timing risk refers to a situation in which the timing of an economically important event is unknown (risky) from the perspective of an economic decision maker. While this special class of dynamic stochastic control problems has many applications in economics, the methods used to solve them are not easily accessible within a single, comprehensive survey. We provide a survey of dynamic optimization methods under comprehensive assumptions about the nature of timing risk. We also relax the assumption of full information and summarize optimization with limited information, ambiguity, imperfect hedging, and dynamic inconsistency. Our goal is to provide a concise user guide for specialists and nonspecialists alike. Full article
(This article belongs to the Special Issue Advances in Theoretical and Empirical Economic Modeling)
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