Journal articles: 'Economics – Simulation methods'

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Heiss, Florian. "Discrete Choice Methods with Simulation." Econometric Reviews 35, no. 4 (February 6, 2016): 688–92. http://dx.doi.org/10.1080/07474938.2014.975634.

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Iannazzo, Sergio. "Bayesian statistic methods and theri application in probabilistic simulation models." Farmeconomia. Health economics and therapeutic pathways 8, no. 1 (March 15, 2007): 5–13. http://dx.doi.org/10.7175/fe.v8i1.251.

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Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

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Odum, Howard T. "Simulation models of ecological economics developed with energy language methods." SIMULATION 53, no. 2 (August 1989): 69–75. http://dx.doi.org/10.1177/003754978905300205.

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Hendershott, Patric H., and Martin Feldstein. "Behavioral Simulation Methods in Tax Policy Analysis." Journal of Money, Credit and Banking 17, no. 2 (May 1985): 280. http://dx.doi.org/10.2307/1992343.

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Novales, Alfonso. "The role of simulation methods in Macroeconomics." Spanish Economic Review 2, no. 3 (December 2000): 155–81. http://dx.doi.org/10.1007/pl00013576.

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Pereira da Silva, Paulo, Paulo Tomaz Rebelo, and Cristina Afonso. "Tail Dependence of Financial Stocks and CDS Markets – Evidence Using Copula Methods and Simulation-Based Inference." Economics: The Open-Access, Open-Assessment E-Journal 8, no. 2014-39 (2014): 1. http://dx.doi.org/10.5018/economics-ejournal.ja.2014-39.

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Calvet, Laura, Rocio de la Torre, Anita Goyal, Mage Marmol, and Angel Juan. "Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review." Administrative Sciences 10, no. 3 (July 30, 2020): 47. http://dx.doi.org/10.3390/admsci10030047.

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Managerial and Business Economics (ME/BE) aims at using quantitative and computational methods to make an efficient (ideally optimal) assignment of the scarce resources owned by firms and organizations. In the current global market, characterized by a fierce competition, an optimal use of the available resources is more important than ever for guaranteeing the economical sustainability of organizations and enterprises of any size. Heuristic optimization algorithms and simulation methods have been successfully employed to analyze and enhance complex systems and processes in a myriad of ME/BE-related fields. This paper reviews recent works on the use of these methodologies in competitive markets, as well as in imperfect markets considering externalities. The paper also discusses open challenges and how state-of-the art methods combining optimization, simulation, and machine learning can contribute to properly address them.

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Davis, Joseph H., Nelson W. Wicas, and Francis M. Kinniry. "The Strengths and Weaknesses of Various Financial Simulation Methods." Journal of Wealth Management 6, no. 4 (January 31, 2004): 33–42. http://dx.doi.org/10.3905/jwm.2004.391056.

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Reutov, Viktor Yevgeniyevich, Viktoriya Vladimirovna Reutova, Larisa Аnatolievna Kravchenko, and Irina Аnatolievna Troyan. "BUSINESS SIMULATION AS AN INTERACTIVE METHOD FOR TRAINING ECONOMISTS." Scientific Bulletin: finance, banking, investment., no. 1 (54) (2022): 162–71. http://dx.doi.org/10.37279/2312-5330-2021-1-162-171.

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n the article, the authors identified the essence of the main categories of research: simulation, computer simulation, business simulation. Business simulation is viewed as an interactive method for training economists, which has a system of rules and objectives and is aimed at forming a set of competencies that will help the student in subsequent economic activities. Business simulation has a clearly defined educational goal — the development of practical skills and competencies by the participants. The authors identified the key advantages of using business simulation, including: differentiation and adaptability of model changes; an integrated approach to the study of an object, verifiability of various approaches, the possibility of solving a variety of problems. The authors noted the disadvantages of using business simulation: psychological characteristics of the transition to innovative methods of training students, poor awareness, lack of digital competencies, low motivation of teachers, problems with access to resources and tools. With the use of a systematic analysis of the advantages and disadvantages, the expediency of introducing business simulation into the educational process in higher educational institutions for students of economic training is substantiated. The authors analyzed the experience of using business simulations popular in foreign universities and business schools, including Blue Ocean Strategy Simulation (BOSS), Capsim Capstone Business Simulation, Capsim Foundation, Cesim, Edumundo, Online Simulations Harvard Business Publishing Reality Works Business Education Simulations, Traction. The authors considered Russian projects of business simulations: Business course: Corporation Plus, Nixdorf «Exchange», Nixdorf «Delta», ViAL +. The authors identified economic disciplines, in teaching which business simulation should be used (accounting and audit, economic theory, finance, enterprise economics, management, trade, investment analysis, marketing). This will allow updating education and bringing new business graduates to the market and, as a consequence, increasing the efficiency of the national economy. It was emphasized that further research requires studying the experience of using business simulation games in universities and business schools

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Chiou, Lesley, and Joan L. Walker. "Masking identification of discrete choice models under simulation methods." Journal of Econometrics 141, no. 2 (December 2007): 683–703. http://dx.doi.org/10.1016/j.jeconom.2006.10.012.

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Geweke, John. "Using simulation methods for bayesian econometric models: inference, development,and communication." Econometric Reviews 18, no. 1 (January 1999): 1–73. http://dx.doi.org/10.1080/07474939908800428.

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Degeling, Koen, Maarten J. IJzerman, Mariel S. Lavieri, Mark Strong, and Hendrik Koffijberg. "Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process." Medical Decision Making 40, no. 3 (April 2020): 348–63. http://dx.doi.org/10.1177/0272989x20912233.

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Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non–health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.

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Gao, Guohua, Jeroen C. Vink, Faruk O. Alpak, and W. Mo. "An Efficient Optimization Work Flow for Field-Scale In-Situ Upgrading Developments." SPE Journal 20, no. 04 (August 20, 2015): 701–16. http://dx.doi.org/10.2118/2014-1885283-pa.

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Summary In-situ upgrading process (IUP) is an attractive technology for developing unconventional extraheavy-oil reserves. Decisions are generally made on field-scale economics evaluated with dedicated commercial tools. However, it is difficult to conduct an automated IUP optimization process because of unavailable interface between the economic evaluator and commercial simulator/optimizer, and because IUP is such a highly complex process that full-field simulations are generally not feasible. In this paper, we developed an efficient optimization work flow by addressing three technical challenges for field-scale IUP developments. The first challenge was deriving an upscaling factor modeled after analytical superposition formulation; proposing an effective method of scaling up simulation results and economic terms generated from a single-pattern IUP reservoir-simulation model to field scale; and validating this approach numerically. The second challenge was proposing a response-surface model (RSM) of field economics to analytically compute key field economical indicators, such as net present value (NPV), by use of only a few single-pattern economic terms together with the upscaling factor, and validating this approach with a commercial tool. The proposed RSM approach is more efficient, accurate, and convenient because it requires only 15–20 simulation cases as training data, compared with thousands of simulation runs required by conventional methods. The third challenge is developing a new optimization method with many attractive features: well-parallelized, highly efficient and robust, and with a much-wider spectrum of applications than gradient-based or derivative-free methods, applicable to problems without any derivative, with derivatives available for some variables, or with derivatives available for all variables. This work flow allows us to perform automated field IUP optimizations by maximizing a full-field economics target while honoring all field-level facility constraints effectively. We have applied the work flow to optimize the IUP development of a carbonate heavy-oil asset. Our results show that the approach is robust and efficient, and leads to development options with a significantly improved field-scale NPV. This work flow can also be applied to other kinds of pattern-based field developments of shale gas and oil, and thermal processes such as steamdrive or steam-assisted gravity drainage.

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Duffuaa, S. O., A. N. Shuaib, and M. Alam. "Evaluation of optimization methods for machining economics models." Computers & Operations Research 20, no. 2 (February 1993): 227–37. http://dx.doi.org/10.1016/0305-0548(93)90077-v.

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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. "Using spreadsheets as learning tools for neural network simulation." Ukrainian Journal of Educational Studies and Information Technology 10, no. 3 (September 30, 2022): 42–68. http://dx.doi.org/10.32919/uesit.2022.03.04.

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The article supports the need for training techniques for neural network computer simulations in a spreadsheet context. Their use in simulating artificial neural networks is systematically reviewed. The authors distinguish between fundamental methods for addressing the issue of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools for neural network simulation, application of third-party add-ins to spreadsheets, development of macros using embedded languages of spreadsheets, use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins, and On the article, methods for creating neural network models in Google Sheets, a cloud-based spreadsheet, are discussed. The classification of multidimensional data presented in R. A. Fisher's "The Use of Multiple Measurements in Taxonomic Problems" served as the model's primary inspiration. Discussed are various idiosyncrasies of data selection as well as Edgar Anderson's participation in the 1920s and 1930s data preparation and collection. The approach of multi-dimensional data display in the form of an ideograph, created by Anderson and regarded as one of the first effective methods of data visualization, is discussed here.

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Suhobokov, Alexander. "APPLICATION OF MONTE CARLO SIMULATION METHODS IN RISK MANAGEMENT." Journal of Business Economics and Management 8, no. 3 (September 30, 2007): 165–68. http://dx.doi.org/10.3846/16111699.2007.9636165.

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The paper deals with Monte Carlo simulation method and its application in Risk Management. The author with the help of MATLAB 7.0 introduces new modification of Monte Carlo algorithm aimed at fast and effective calculation of financial organization's Value at Risk (VaR) by the example of Parex Bank's FOREX exposure.

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Storm, Hugo, Kathy Baylis, and Thomas Heckelei. "Machine learning in agricultural and applied economics." European Review of Agricultural Economics 47, no. 3 (August 21, 2019): 849–92. http://dx.doi.org/10.1093/erae/jbz033.

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AbstractThis review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.

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Gamba-Santamaria, Santiago, Oscar Fernando Jaulin-Mendez, Luis Fernando Melo-Velandia, and Carlos Andrés Quicazán-Moreno. "Comparison of methods for estimating the uncertainty of value at risk." Studies in Economics and Finance 33, no. 4 (October 3, 2016): 595–624. http://dx.doi.org/10.1108/sef-03-2016-0055.

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Purpose Value at risk (VaR) is a market risk measure widely used by risk managers and market regulatory authorities, and various methods are proposed in the literature for its estimation. However, limited studies discuss its distribution or its confidence intervals. The purpose of this paper is to compare different techniques for computing such intervals to identify the scenarios under which such confidence interval techniques perform properly. Design/methodology/approach The methods that are included in the comparison are based on asymptotic normality, extreme value theory and subsample bootstrap. The evaluation is done by computing the coverage rates for each method through Monte Carlo simulations under certain scenarios. The scenarios consider different persistence degrees in mean and variance, sample sizes, VaR probability levels, confidence levels of the intervals and distributions of the standardized errors. Additionally, an empirical application for the stock market index returns of G7 countries is presented. Findings The simulation exercises show that the methods that were considered in the study are only valid for high quantiles. In particular, in terms of coverage rates, there is a good performance for VaR(99 per cent) and bad performance for VaR(95 per cent) and VaR(90 per cent). The results are confirmed by an empirical application for the stock market index returns of G7 countries. Practical implications The findings of the study suggest that the methods that were considered to estimate VaR confidence interval are appropriated when considering high quantiles such as VaR(99 per cent). However, using these methods for smaller quantiles, such as VaR(95 per cent) and VaR(90 per cent), is not recommended. Originality/value This study is the first one, as far as it is known, to identify the scenarios under which the methods for estimating the VaR confidence intervals perform properly. The findings are supported by simulation and empirical exercises.

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Martin, G. M., and C. S. Forbes. "Using simulation methods for bayesian econometric models: inference, development and communication: some comments." Econometric Reviews 18, no. 1 (January 1999): 113–18. http://dx.doi.org/10.1080/07474939908800433.

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Zhou, Ming-Yang, Xiao-Yu Li, Wen-Man Xiong, and Hao Liao. "Quantifying the Robustness of Countries’ Competitiveness by Network-Based Methods." Complexity 2018 (December 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/5738135.

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In economic researches, much effort was devoted to the problem of how to increase the economics of countries. However, the development of a country may fluctuate a lot due to international and domestic problems. Thus, we should also evaluate the robustness of countries against unexpected economic recessions. In this paper, we use perturbation to quantify the robustness of countries using two renowned algorithms: method of reflections (MR) and fitness-complexity method (FCM). The robustness characterizes the stability of countries’ competitiveness against economic recessions. The experiments in the international trade networks show that FCM could characterize the robustness better than MR. High fitness countries of FCM have strong robustness against economic crises, which enlarges the application fields of FCM. Additionally, we simulate the trade conflict between USA and China. The simulation results show that China suffers much in the trade conflict, while USA loses very little and has strong robustness in this conflict.

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Budiwati, Neti, Kinanti Geminastiti H, and Fazar Nuriansyah. "USE OF SIMULATION METHODS AND VIDEO MEDIA TEACHING IN INCREASING STUDENTS' INTEREST TO BECOME TEACHERS (Case in Economics Learning Strategy Course)." AL-ISHLAH: Jurnal Pendidikan 14, no. 3 (August 15, 2022): 3813–20. http://dx.doi.org/10.35445/alishlah.v14i3.1680.

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The problem of the low interest in being a teacher for students participating in the Economics Learning Strategy Study Program of Economics Education Study Program at the University of Economics Education became the motivation for this research to be carried out. This study aims to determine whether the use of teaching simulation methods and teaching video media for teachers can increase students' interest in being a teacher. The research method is descriptive qualitative with an open questionnaire data collection tool through the Integrated Online Learning System (SPOT) Universitas Pendidikan Indonesia. The research subjects were 88 students participating in the Economics Learning Strategy course. The results showed that the student's interest in becoming a teacher after lectures using teaching simulation methods and teaching video media was on average in the high classification, and a change in interest in becoming a teacher was 80.62%. It can be concluded that the use of teaching simulation methods and teaching video media in the Economics Learning Strategy course increases students' interest in becoming teachers.

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Berry, Steven T., and Giovanni Compiani. "Empirical Models of Industry Dynamics with Endogenous Market Structure." Annual Review of Economics 13, no. 1 (August 5, 2021): 309–34. http://dx.doi.org/10.1146/annurev-economics-081720-120019.

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This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure follows from the presence of serially correlated unobservable shocks to the profitability of firms’ dynamic decisions, a feature common to many empirical settings. Methods that ignore endogeneity can lead to misleading parameter estimates and misleading counterfactual results. We pay particular attention to extensions of standard two-step methods that leverage instrumental variables to address endogeneity in both single-agent and oligopoly models. A first step set-identifies dynamic policy functions together with serial correlation parameters, and a second step quickly solves for profit function parameters using an extension of existing forward-simulation methods. We discuss how these new methods provide a general solution to initial-conditions problems and how they can yield practical estimation strategies.

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Ludkovski, Michael, and Aditya Maheshwari. "Simulation methods for stochastic storage problems: a statistical learning perspective." Energy Systems 11, no. 2 (January 3, 2019): 377–415. http://dx.doi.org/10.1007/s12667-018-0318-4.

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Subramanian, Avinash, Truls Gundersen, and Thomas Adams. "Modeling and Simulation of Energy Systems: A Review." Processes 6, no. 12 (November 23, 2018): 238. http://dx.doi.org/10.3390/pr6120238.

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Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.

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Kislitsyn, Evgeniy, and Victor Gorodnichev. "Scenarios of sustainable development of metallurgical industry: simulation modeling." E3S Web of Conferences 208 (2020): 03012. http://dx.doi.org/10.1051/e3sconf/202020803012.

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Due to a complicated epidemiological situation, current economic situation in Russia and the world gives a push to new researches in the field of ensure sustainable development in all branches of industry. Methods and tools available today cannot fully provide this, so modern economics needs to develop fundamentally new approaches to the study of industries. Within the framework of this research it is proposed to use simulation modeling to predict scenarios of sustainable development of certain industries, in particular metallurgy, mining of metal ores and production of finished metal products. The research is based on systemic-synergistic approach based on the theory of industrial organization, economic growth and development, system dynamics and mathematical economics. The simulation model of metallurgy and related industries development, which is based on a system dynamic flow – streaming stratification, was designed and developed as a result of this research. Ideas of three-sector model, adapted to the sectoral aspects of the functioning of the economic system, are used in the simulation model. The development allows to simulate various scenarios of the development of the industries under study in order to determine the trajectory of their sustainable development. As a demonstration, three scenarios of the development of industries are presented, taking into account changes in the most important component of the economic system - labor force.

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Jagathesan, Dr T. "A critical study of the evolution of Mathematics in Economic Analysis." JOURNAL OF DEVELOPMENT ECONOMICS AND MANAGEMENT RESEARCH STUDIES 08, no. 08 (2021): 78–82. http://dx.doi.org/10.53422/jdms.2021.8801.

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Mathematical economics is an electrifying division of study in economics. It is helpful in model building and provides mathematical form of a descriptive theory in a simple as well as easily understandable way. Economics axiomatization can be embedded with mathematical formula to make it more scientific. Some of the concepts and theories like input-output analysis, linear programming, theory of games and economic behaviour, economic problems of optimum allocation of resources, organizing and planning of production, hyper formalistic methods now the application of computer simulation, normative economics ideas into positive economics for testing its validity given the normative assumptions, and failure of some mathematical models. This paper discusses about the evolution of mathematical economics critically.

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Okrepilov, V. V., V. L. Makarov, A. R. Bakhtizin, and S. N. Kuzmina. "Application of Supercomputer Technologies for Simulation Of Socio-Economic Systems." Economy of Region 17, no. 4 (2021): 1405. http://dx.doi.org/10.17059/ekon.reg.2021-4-25.

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In order to сover a more wide thematic audience of the journal Economy of Regiona, the authors Okrepilov V. V., Makarov V. L., Bakhtizin A. R., Kuzmina S.N. of the paper Application of Supercomputer Technologies for Simulation Of Socio-Economic Systems, published in the 2nd Issue, 2015 used the materials of previously published paper “Supercomputer Technologies Application in Social Sciences”, Economics and Mathematical Methods, Vol.49 (4), 2013 and “Supercomputer Technologies Application in Social Sciences”, UPRAVLENIE / MANAGEMENT (Russia), Issue 2, 2014 (DOI 10.12737/4167). The works used the same methods developed by the authors, but different results were obtained. A link to a previously published articles is included in the electronic version of the article Okrepilov V. V., Makarov V. L., Bakhtizin A. R., Kuzmina S.N. (2015). Application of Supercomputer Technologies for Simulation Of Socio-Economic Systems. Economy of Region [Economy of region], 2, 301-312.

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Babenko, Vitalina, Andriy Panchyshyn, Larysa Zomchak, Maryna Nehrey, Zoriana Artym-Drohomyretska, and Taras Lahotskyi. "Classical Machine Learning Methods in Economics Research: Macro and Micro Level Examples." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 18 (January 5, 2021): 209–17. http://dx.doi.org/10.37394/23207.2021.18.22.

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Paper reviews the classical methods of machine learning (supervised and unsupervised learning),gives examples of the application of different methods and discusses approaches that will be useful for empiricaleconomics research (on data from Ukrainian firms, banks and official state statistics). The different sectors ofeconomics are investigated: the multiple linear regression is used on macrolevel for macro production functionof Ukraine specification; logistic regression is used in bank sector for credit risk management with the scoringmodel; k-means, hierarchic clustering and DBSCAN are used in regional level for regions of Ukraine groupingbased on competitiveness; principal component analysis is used for firm’s financial stability analysis. All modelsshowed adequate simulation results according to the quality criteria of the models. So, the possibility ofclassic machine learning methods application for investigations of the processes and objects on different levelsof economics (micro, mezzo and macro) is demonstrated in the article.

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Wang, Jianguo, Tongsan Wang, Yuna Shi, Diwei Xu, Yutian Chen, and Jie Wu. "Metaverse, SED Model, and New Theory of Value." Complexity 2022 (May 9, 2022): 1–26. http://dx.doi.org/10.1155/2022/4771516.

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The metaverse concept constructs a virtual world parallel to the real world. The social economic dynamics (SED) model establishes a systematic model for social economic dynamics simulation that integrates macroeconomy and microeconomy based on modeling mechanism of the new theory of value by analogy with Newtonian mechanics and the modeling approach of Agent-based computational economics. This article describes the SED model’s modeling mechanisms, modeling rules, and behavior equations. At the same time, this article introduces the methods, testing standards, and some typical cases about using the SED model to generate the economic digital twin systems. By doing so, we hope to demonstrate that the method of computer simulation experiment based on the SED model is a scientific empirical method, which has more advantages than the existing empirical research methods in economics. The SED model, which can be fully used to form an economic engine and construct a virtual economic system by digital twin method, can be integrated with the extant physical engine in the metaverse concept to build a virtual world consisting of physics, economy, culture, and politics that is close to and coexists with reality.

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Weinberg, Matthew C. "More Evidence on the Performance of Merger Simulations." American Economic Review 101, no. 3 (May 1, 2011): 51–55. http://dx.doi.org/10.1257/aer.101.3.51.

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Merger simulations are commonly used to simulate the effects of potential mergers. Despite the large resources devoted to merger review, little evidence exists on the accuracy of these methods. This paper uses the acquisition of Tambrands by Proctor and Gamble to provide evidence on the efficacy of merger simulation. Two simple demand systems are estimated under several identification assumptions and combined with a static model of price competition. Simulations predict small price effects of about 1 percent for the merging firms' brands, while direct estimates indicate the merger raised prices by 5–8 percent.

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Asmussen, S., and K. Binswanger. "Simulation of Ruin Probabilities for Subexponential Claims." ASTIN Bulletin 27, no. 2 (November 1997): 297–318. http://dx.doi.org/10.2143/ast.27.2.542054.

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AbstractWe consider the classical risk model with subexponential claim size distribution. Three methods are presented to simulate the probability of ultimate ruin and we investigate their asymptotic efficiency. One, based upon a conditional Monte Carlo idea involving the order statistics, is shown to be asymptotically efficient in a certain sense. We use the simulation methods to study the accuracy of the standard Embrechts-Veraverbeke [16] approximation for the ruin probability and also suggest a new one based upon ideas of Hogan [21].

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Gansterer, Margaretha, Christian Almeder, and Richard F. Hartl. "Simulation-based optimization methods for setting production planning parameters." International Journal of Production Economics 151 (May 2014): 206–13. http://dx.doi.org/10.1016/j.ijpe.2013.10.016.

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Kislitsyn, Evgeniy, and Victor Gorodnichev. "Simulation of development of individual heavy industry sectors." Business Informatics 15, no. 1 (March 31, 2021): 59–77. http://dx.doi.org/10.17323/2587-814x.2021.1.59.77.

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Nowadays, in the context of the coronavirus crisis, the issue of ensuring the sustainable development of heavy industries is acute. However, theoretical and analytical researches alone are not sufficient for this, and economic science needs to develop fundamentally new approaches to the study of the development of industrial sectors. This article is devoted to the creation and testing of a simulation model for the development of individual sectors of the economy. The object of research is the metallurgical industry, as well as related ore mining, mechanical engineering and production of finished metal products. The theoretical basis of the research is a systematic approach that combines the theory of industry markets, economic growth, industrial economics, system dynamics and mathematical economics. The main research methods used are system analysis, statistical analysis to identify trends in changes in the main economic indicators, econometric modeling to build production functions, as well as mathematical modeling of macroeconomic systems. As a result, a simulation model developed in system dynamics notation is proposed, which makes it possible to evaluate the development of individual industries taking into account various changes. This model is built on the basis of the three-sector model of the national economy, where separate adjacent industries connected by dynamic feedback loops are identified as structural elements. The paper details the structure of the simulation model based on first-order dynamic equations, balance equations and nonlinear production functions. The simulation model allowed us to predict a number of scenarios for the development of metallurgical industries, taking into account changes in the labor force and investment in fixed assets. The results of the work can be used for forming proposals on industrial policy, monitoring the condition and efficiency of individual industries.

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Griffiths, W. E. "Estimating consumer surplus comments on "using simulation methods for bayesian econometric models: inference development and communication"." Econometric Reviews 18, no. 1 (January 1999): 75–87. http://dx.doi.org/10.1080/07474939908800429.

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Lollivier, Stefan. "Dynamics of Individual Wealth Accumulation: A Use of Simulation Methods for Estimating Limited Dependent-Variables Models." Economica 71, no. 284 (November 2004): 589–618. http://dx.doi.org/10.1111/j.0013-0427.2004.00390.x.

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Backhaus, Klaus, Robert Wilken, and Thomas Hillig. "Predicting Purchase Decisions with Different Conjoint Analysis Methods: A Monte Carlo Simulation." International Journal of Market Research 49, no. 3 (May 2007): 341–64. http://dx.doi.org/10.1177/147078530704900306.

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Ekelund, Stale, and Zilia Iskoujina. "Cybersecurity economics – balancing operational security spending." Information Technology & People 32, no. 5 (October 7, 2019): 1318–42. http://dx.doi.org/10.1108/itp-05-2018-0252.

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Purpose The purpose of this paper is to demonstrate how to find the optimal investment level in protecting an organisation’s assets. Design/methodology/approach This study integrates a case study of an international financial organisation with various methods and theories in security economics and mathematics, such as value-at-risk (VaR), Monte Carlo simulation, exponential and Poisson probability distributions. Thereby it combines theory and empirical findings to establish a new approach to determining optimal security investment levels. Findings The results indicate that optimal security investment levels can be found through computer simulation with historical incident data to find VaR. By combining various scenarios, the convex graph of the risk cost function has been plotted, where the minimum of the graph represents the optimal invest level for an asset. Research limitations/implications The limitations of the research include a modest number of loss observations from one case study, and the use of normal probability distribution. The approach has limitations where there are no historical data available or the data has zero losses. These areas should undergo further research including larger data set of losses and exploring other probability distributions. Practical implications The results can be used by leading business practitioners to assist them with decision making on investment to the increased protection of an asset. Originality/value The originality of this research is in its new way of combining theories with historical data to create methods to measure theoretical and empirical strength of a control (or set of controls) and translating it to loss probabilities and loss sizes.

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Bulygina, Olga V., and Ekaterina A. Vlasova. "Features of building hybrid simulation models in Actor Pilgrim." Journal Of Applied Informatics 17, no. 1 (97) (January 30, 2022): 109–16. http://dx.doi.org/10.37791/2687-0649-2022-17-1-109-116.

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In recent years, simulation has been actively used to study socio-economic processes in order to test various management decisions (for analyzing risks, projects, regional processes, logistics, etc.). Today, three simulation systems (Actor Pilgrim, AnyLogic, GPSS World), each of which has its own areas of application, are the most widespread in Russia. So, the system Actor Pilgrim is most suitable for modeling socio- economic processes. The first version of this system was developed by a group led by Professor Alexander Anatolyevich Yemelyanov more than 35 years ago to solve experimental problems in the direction of "Flexible automated production". It was based on a new modeling paradigm, which was built on the actor-network theory. The transition to solving new problems, primarily in the economics, led to the need for its further system development through the implementation of temporal, financial and spatial dynamics. Currently, the system development is carried out through the construction of hybrid simulation models, which is associated with the introduction of various analysis methods. So, when modeling actual technical and economic processes (for example, import substitution of high- tech products), it is proposed to use artificial intelligence methods that allow you to get informed decisions in conditions of information uncertainty. Models that include fuzzy logic methods and swarm algorithms (in particular, bacterial optimization) have shown good results. For example, fuzzy logic methods have been used to assign "fair" priorities to option projects through a detailed analysis of the factors of the internal and external environment of enterprises that will implement them. Bacterial optimization algorithms have been used to search for "promising" areas for the implementation of the projects of import substitution of high-tech products. These swarm algorithms are distinguished by the ability to simultaneously study favorable and negative factors, i. e. allow taking into account various risk situations. The modern version of Actor Pilgrim is intended for systems analysts, economists-mathematicians and other professionals who are familiar with programming, but are not professional programmers.

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Fernández-Reyes, Bernardo A., Ana K. Flores-González, Luis Adrian Alvarez-Lozada, Juventino T. Guerrero-Zertuche, Francisco J. Arrambide-Garza, Xavier G. Quiroz-Perales, Alejandro Quiroga-Garza, Rodrigo E. Elizondo-Omaña, and Santos Guzmán-López. "The importance of simulation training in surgical sciences." International Surgery Journal 9, no. 6 (May 26, 2022): 1289. http://dx.doi.org/10.18203/2349-2902.isj20221430.

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Simulators have been used throughout history to practice complicated procedures before performing them on human beings. The earliest simulation attempts were in cadavers. Donor bodies are still used for teaching and research but involve costly infrastructure, ethical and legal issues, as well as animal models. Training models need to be purposefully designed. These can be physical models, 3-D printed, simulators with virtual reality, augmented reality, or a hybrid simulation. The inert model is an alternative for animal tissue models, based on a trial-and-error method, the learning curve is approximately 65 procedures for a laparoscopist. Simulations models with virtual and augmented reality have shown that can reduce the time of practitioners with experience in laparoscopy, with an approximate reduction of 30 to 58%. Video-based learning method has been adopted in recent years but has shown to be less effective than hand-on learning using a simulator. Simulation can be involved to simulate specific scenarios, recreate simulated trauma patients, help develop a doctor-patient relationship and prepare complex approaches. Patient safety concerns call for the need to train medical personnel in simulated settings to reduce cost and patient morbidity because the ability to acquire surgical skills requires consistent practice. Simulation represents ideal teaching methods to optimize the knowledge and skill of residents before they are entrusted with procedures with real patients.

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Dufresne, François, and Hans U. Gerber. "Three Methods to Calculate the Probability of Ruin." ASTIN Bulletin 19, no. 1 (April 1989): 71–90. http://dx.doi.org/10.2143/ast.19.1.2014916.

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AbstractThe first method, essentially due to GOOVAERTS and DE VYLDER, uses the connection between the probability of ruin and the maximal aggregate loss random variable, and the fact that the latter has a compound geometric distribution. For the second method, the claim amount distribution is supposed to be a combination of exponential or translated exponential distributions. Then the probability of ruin can be calculated in a transparent fashion; the main problem is to determine the nontrivial roots of the equation that defines the adjustment coefficient. For the third method one observes that the probability, of ruin is related to the stationary distribution of a certain associated process. Thus it can be determined by a single simulation of the latter. For the second and third methods the assumption of only proper (positive) claims is not needed.

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Zimmermannova, Jarmila, Andreas P. Redecker, Michal Mensik, and Carsten Juergens. "Geospatial Data Analysis and Economic Evaluation of Companies for Sustainable Business Development—An Interdisciplinary Teaching Approach." Sustainability 13, no. 20 (October 12, 2021): 11245. http://dx.doi.org/10.3390/su132011245.

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Sustainable business development is connected with environmental impact, natural resources and people. This makes the location a crucial factor for the operation of a business. Therefore, a combination of both geo-spatial data analysis and traditional economic evaluation of companies are advantageous. The consideration of geolocation is beneficial with calculations for process optimizations and cost efficiency as well as ecological and social compliance. Since integrating geospatial methods into economics is a rather new interdisciplinary approach, it seems necessary to establish innovative teaching concepts for the education of experts in this field. Creating and testing such new teaching concepts based on playful learning is the idea behind the ERASMUS+ project SPATIONOMY (“Spatial exploration of economic data—methods of interdisciplinary analytics”). An interdisciplinary team of teachers educates an interdisciplinary assembled group of international students. Hence, the fields of economics/business informatics and geography/geomatics are represented by participants and staff. Based on initial lessons about basic knowledge in the connected subjects, the central elements of the teaching concept are case studies and a simulation game, each with interdisciplinary challenges. The principal aim of the project—to educate students to become specialists in spatial economics—could be achieved. This paper aims to present, evaluate and discuss the methodological approach as well as the results from the application of the simulation game. The results show that the gamification of education is worthy. Simulation game-based learning appears to be more playful and experiential compared to traditional teaching approaches. Further research in this area should focus on the students’ engagement evaluation and attitude towards sustainable behaviour in their own business.

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Zimmermannova, Jarmila, Andreas P. Redecker, Michal Mensik, and Carsten Juergens. "Geospatial Data Analysis and Economic Evaluation of Companies for Sustainable Business Development—An Interdisciplinary Teaching Approach." Sustainability 13, no. 20 (October 12, 2021): 11245. http://dx.doi.org/10.3390/su132011245.

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Sustainable business development is connected with environmental impact, natural resources and people. This makes the location a crucial factor for the operation of a business. Therefore, a combination of both geo-spatial data analysis and traditional economic evaluation of companies are advantageous. The consideration of geolocation is beneficial with calculations for process optimizations and cost efficiency as well as ecological and social compliance. Since integrating geospatial methods into economics is a rather new interdisciplinary approach, it seems necessary to establish innovative teaching concepts for the education of experts in this field. Creating and testing such new teaching concepts based on playful learning is the idea behind the ERASMUS+ project SPATIONOMY (“Spatial exploration of economic data—methods of interdisciplinary analytics”). An interdisciplinary team of teachers educates an interdisciplinary assembled group of international students. Hence, the fields of economics/business informatics and geography/geomatics are represented by participants and staff. Based on initial lessons about basic knowledge in the connected subjects, the central elements of the teaching concept are case studies and a simulation game, each with interdisciplinary challenges. The principal aim of the project—to educate students to become specialists in spatial economics—could be achieved. This paper aims to present, evaluate and discuss the methodological approach as well as the results from the application of the simulation game. The results show that the gamification of education is worthy. Simulation game-based learning appears to be more playful and experiential compared to traditional teaching approaches. Further research in this area should focus on the students’ engagement evaluation and attitude towards sustainable behaviour in their own business.

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PUCHKOV, Evgeniy Vladimirovich, Natal'ya Alekseyevna OSADCHAYA, and Anton Dmitrievich MURZIN. "Engineering Simulation of Market Value of Construction Materials." Journal of Advanced Research in Law and Economics 9, no. 6 (November 1, 2019): 2096. http://dx.doi.org/10.14505//jarle.v9.6(36).25.

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Material resources are one of the main elements of the construction cost estimate. The nomenclature of material resources includes a large number of items. It is impossible to objectively estimate the construction cost, capital construction, and maintenance works without carrying out monitoring of the prices of constructional resources and accounting for these data in estimates. The purpose of the present study consists in developing an engineering approach to the simulation of pricing in the construction materials’ market. To achieve this purpose, we use mathematical statistics methods such as linear regression and autoregressive integrated moving average, as well as machine learning methods, namely gradient boosting and recurrent neural networks. In consequence of this work, we proposed a scheme to store statistical information based on the SpagoBI business intelligence platform, as well as designed hybrid intelligent predictive model, which allowed automating the engineering approach to the prediction of prices and objectifying advanced analytics of the construction materials’ market. The proposed engineering approach allows predicting the dynamics of the construction materials’ market segments when managing the construction cost at the level of enterprise and the region that will enable adequate decision- making in the course of investment projects development.

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Bonfiglio, Andrea, and Francesco Chelli. "Assessing the Behaviour of Non-Survey Methods for Constructing Regional Input–Output Tables through a Monte Carlo Simulation." Economic Systems Research 20, no. 3 (September 2008): 243–58. http://dx.doi.org/10.1080/09535310802344315.

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Johansyah, Muhamad Deni, Asep K. Supriatna, Endang Rusyaman, and Jumadil Saputra. "Modelling of natural growth with memory effect in economics: An application of adomian decomposition and variational iteration methods." Decision Science Letters 11, no. 1 (2022): 73–80. http://dx.doi.org/10.5267/j.dsl.2021.8.003.

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The power-law memory effect is taken into consideration in a generalisation of the economic model of natural growth. The memory effect refers to a process's reliance on its current state and its history of previous changes. However, the study that focuses on natural growth in economics considering the memory effect with fractional order-linear differential equation model is still limited. The current investigation seeks to solve the natural growth with memory effect in the economics model and decide the best model using fractional differential equation (FDE), namely Adomian Decomposition and Variational Iteration Methods. Also, this study assumes the level of consumer loss memory during a certain time interval denoted by a parameter (α). This study showed the model of loss memory effect with 0 < α ≤ 1 given a slowdown in output growth compared to a model without memory effect. Besides that, this study also found that output Y(t) is growing faster with the Variational Iteration method compared to the Adomian decomposition method. Also, using graphical simulation, this study found the output Y(t) is closer to the exact solution with α=0.4 and α=0.9. In conclusion, this study successfully solved natural growth with memory effect in economics and decided the best model between FDE, namely Adomian decomposition and Variational iterative methods using numerical analysis.

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Riadynska, I. A. "The Innovative Teaching Methods in the Study of Economic Disciplines in Higher Education Institutions of Ukraine." Business Inform 1, no. 516 (2021): 134–38. http://dx.doi.org/10.32983/2222-4459-2021-1-134-138.

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The article is primarily aimed at considering the essence and significance of innovative methods of teaching economic disciplines in higher education institutions, their use in professional activities with students during educational work. The article considers innovative methods of learning in the teaching of economic disciplines used in the system of modern educational process in higher education institutions of Ukraine. The peculiarities of forms and methods of innovative learning are defined, the specifics of their use during classes with students in economic disciplines are covered. The author specifies application of such methods, forms and methods of educational work as: audiovisual method of training; analysis of errors, collisions, incidents; discussion with the invitation of specialists; brainstorming; «decision tree»; business (role-playing) game (students try the role of a teacher in economics, an accountant, auditor, a broker, manager, etc.); «take a position»; Socratic dialogue; interview method; public speaking; commenting, evaluating (or self-evaluating) of the participants’ actions; training ground; method of analysis and diagnosis of the situation; method of cooperative learning; problematic methods; partially search methods; research methods; method of projects; modeling; trainings; the program-role method of generating ideas; work in small groups; «competence» game; simulation games; PRES formula, etc. The essence of the case method, its principles, structure, features, influence on the formation of qualitative characteristics of the future specialist in economics is covered. It is emphasized the use of new technologies in learning, such as multimedia, computers, etc. and the combination of innovative methods with classical, traditional ones.

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Yun, Jaeho. "Density Forecast Evaluations via a Simulation-Based Dynamic Probability Integral Transformation*." Journal of Financial Econometrics 18, no. 1 (November 28, 2018): 24–58. http://dx.doi.org/10.1093/jjfinec/nby030.

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Abstract This paper presents simulation-based density forecast evaluation methods using particle filters. The simulation-based dynamic probability integral transformation or log-likelihood evaluation method is combined with the existing density forecast evaluation methods. This methodology is applicable to various density forecast models, such as log stochastic volatility models and affine jump diffusion (AJD) models, for which the probability integral transform or likelihood computation is difficult due to the presence of latent stochastic volatilities. This methodology is applied to the daily S&P 500 stock index. The empirical results show that the AJD models with jumps perform the best for out-of-sample evaluations.

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Dominguez, Gustavo, Humberto García, Francisco Martínez, and Cynthia Márquez. "GAPS IN SIMULATION INTERFACES FOR PROCEDURES, METHODS AND TECHNIQUES IN MANUFACTURE ASSISTED BY COMPUTERS." Journal of International Business and Economics 20, no. 2 (June 1, 2020): 17–24. http://dx.doi.org/10.18374/jibe-20-2.2.

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Yang, Ya-Juan, Chung-Cheng Chen, and Yen-Ting Chen. "New Method of Solving the Economic Complex Systems." Discrete Dynamics in Nature and Society 2020 (November 29, 2020): 1–26. http://dx.doi.org/10.1155/2020/8827544.

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In this study, the authors first develop a direct method used to solve the linear nonhomogeneous time-invariant difference equation with the same number for inputs and outputs. Economic cybernetics is the crystallization for the integration of economics and cybernetics. It analyzes the stability, controllability, and observability of the economic system by establishing a system model and enables people to better understand the characteristics of the economic system and solve economic optimization problems. The economic model generally applies the discrete recurrence difference equation. The significant analytic approach for the difference equation is the z-transformation technique. The z-transformation state of the economic cybernetics state-space difference equation generally is a rational function with the same power for the numerator and the denominator. The proposed approach will take the place of the traditional methods without all annoying procedures involving the long division of some complicated polynomials, the expanded multiplication of many polynomial factors, the differentiation of some complicated polynomials, and the complex derivations of all partial fraction parameters. To highlight the novelty of this research, this study especially applies the proposed theorems originally belonging to engineering to the field of economic applications.

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Czekirda, Marta, Patrycja Misztal-Okońska, Anna Włoszczak-Szubzda, Mariusz Goniewicz, Mateusz Cybulski, Krystyna Kowalczuk, Noemi Jaszyna, et al. "Objective and Subjective Stress Parameters in Response to High and Low-Fidelity Simulation Activities." International Journal of Environmental Research and Public Health 19, no. 5 (March 3, 2022): 2980. http://dx.doi.org/10.3390/ijerph19052980.

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Nursing graduates are required to have both excellent theoretical and practical skills that should be used during stressful emergency interventions. Since the received knowledge should be practiced to gain skills and trained to achieve competences, simulation exercises can be beneficial to even reduce the stress that each individual may face during emergency management of patients. A total of 146 first-year nursing students participated in the study, including 124 women and 22 men aged between 19 and 50 years, with a mean age of 32 years. The objective method estimated psychophysiological parameters (serum cortisol). Objective and subjective methods were used. The subjective method assessed stress experienced by students based on the standardized Stress Appraisal Questionnaire Version B for dispositional assessment. The study was conducted in the Monoprofile Medical Simulation Centre at the University of Economics and Innovation in Lublin, Poland and was approved by the University Research Ethics Committee. Both participants under and over 25 years of age showed increased levels of stress after low and high-fidelity simulations, with statistically significantly higher stress levels found for the low fidelity method. Low-fidelity simulation methods generated a greater increase in cortisol levels, indicating a higher stress level than the high-fidelity methods. The analysis of the scores obtained in the Stress Appraisal Questionnaire (KOS-B) showed that higher cortisol levels after the low-fidelity simulation reduced the subjective perception of a threat, while higher cortisol levels before the high-fidelity simulation promoted higher intellectual activity among the students. Levels of stress in the education of nursing students using low and high-fidelity methods can limit the sense of threat and activate professional task performance. The use of low and high-fidelity simulation does not generate destructive stress levels.

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Journal articles on the topic 'Economics – Simulation methods'

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