Relevant bibliographies by topics / Econometric modeling

Academic literature on the topic 'Econometric modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Econometric modeling"

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Jorgenson, Dale W. "Econometric general equilibrium modeling." Journal of Policy Modeling 38, no. 3 (May 2016): 436–47. http://dx.doi.org/10.1016/j.jpolmod.2016.02.004.

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QIN, Duo. "BAYESIAN ECONOMETRICS: The First Twenty Years." Econometric Theory 12, no. 3 (August 1996): 500–516. http://dx.doi.org/10.1017/s0266466600006836.

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This paper sketches the history of how Bayesian inference was adopted and utilized in econometrics during its first 20 years. It focuses on the causes of the Bayesian movement, the ways in which Bayesian inference was applied, the problems that the application was intended to solve, and the results achieved. It shows that Bayesian research has largely followed mainstream econometric development as far as the major econometric ideas and methods are concerned and that Bayesian reformulation of mainstream econometrics has nevertheless helped in deepening econometricians' understanding of many modeling problems by presenting them from a different angle.
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Lady, George M., and Carlisle E. Moody. "Econometric Modeling and Model Falsification." Advances in Pure Mathematics 09, no. 09 (2019): 762–76. http://dx.doi.org/10.4236/apm.2019.99036.

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Yusov, Anatoly B., and Antonina A. Kasatkina. "MODELING CYCLES IN ECONOMETRIC MODELS." Statistics and Economics, no. 1 (January 1, 2015): 176–78. http://dx.doi.org/10.21686/2500-3925-2015-1-176-178.

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Klein, Lawrence R., and Fyodor I. Kushnirsky. "Econometric modeling at mixed frequencies." Journal of Economic and Social Measurement 30, no. 4 (December 29, 2005): 251–77. http://dx.doi.org/10.3233/jem-2005-0258.

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Cai, Zongwu, Yongmiao Hong, and Shouyang Wang. "Econometric Modeling and Economic Forecasting." Journal of Management Science and Engineering 3, no. 4 (December 2018): 178–82. http://dx.doi.org/10.3724/sp.j.1383.304010.

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Jin, Hui, and Dale W. Jorgenson. "Econometric modeling of technical change." Journal of Econometrics 157, no. 2 (August 2010): 205–19. http://dx.doi.org/10.1016/j.jeconom.2009.12.002.

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McFadden, Daniel. "Econometric modeling of locational behavior." Annals of Operations Research 18, no. 1 (December 1989): 1–15. http://dx.doi.org/10.1007/bf02097792.

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Klein, L. R. "Econometric Modeling at Mixed Frequencies." Journal of Mathematical Sciences 133, no. 4 (March 2006): 1445–48. http://dx.doi.org/10.1007/s10958-006-0059-0.

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Balash, O. S. "Econometric Modeling of Spatial Interaction." Izvestiya of Saratov University. Economics. Management. Law 12, no. 3 (2012): 30–35. http://dx.doi.org/10.18500/1994-2540-2012-12-3-30-35.

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Dissertations / Theses on the topic "Econometric modeling"

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Lytsenko, M., Тетяна Олександрівна Маринич, Татьяна Александровна Маринич, and Tetiana Oleksandrivna Marynych. "Econometric modeling of nonstationary processes." Thesis, Karazin National University, 2015. http://essuir.sumdu.edu.ua/handle/123456789/68631.

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Econometric research of nonstationary time series on causality, cointegration relation and adequate simulation methods was conducted. VAR and VEC models were found to be the most appropriate ways to make reliable prediction and scenario analysis of macro financial data under unstable economic conditions. These econometric techniques were approbated on the financial indicators of Ukrainian economy.
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Sutter, Ryan C. "Spatial Econometric Modeling of Presidential Voting Outcomes." University of Toledo / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1114618256.

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Yoldas, Emre. "Essays on multivariate modeling in financial econometrics." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663051691&SrchMode=2&sid=2&Fmt=6&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1265225972&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2008. Thesis (Ph. D.)--University of California, Riverside, 2009.
Includes abstract. Title from first page of PDF file (viewed February 3, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 135-137). Includes bibliographical references (leaves ). Also issued in print.
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Blöchlinger, Andreas. "Econometric advancements in market and credit risk modeling /." [S.l.] : [s.n.], 2005. http://aleph.unisg.ch/hsgscan/hm00147040.pdf.

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LeSage, James P., and Manfred M. Fischer. "Spatial econometric methods for modeling origin destination flows." WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/3957/1/SSRN%2Did1304571.pdf.

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Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models. (authors' abstract)
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Sousa, Isaac Figueiredo de. "Econometric modeling of the balance of social security Brazil." Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=4302.

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nÃo hÃ
This work aims to build models using econometrics techniques to explain the components of the balance of Social Security System, or in other words, the net value of tax revenues and the benefit values of the General Regime of Social Security. These models were subjected to statistic validations indicated in the theoretical reference of econometrics, to apply the method of ordinary least square from the classic model of linear regression. From an increasing longevity and the gradual decrease in the birth rate for Brazilian citizens, forming an almost biometrics conspiracy, combined with the benevolent rules for granting benefits, the projections show that pensions expenses will remain above the revenue and the difference between the two will increase year after year. These projections however, depend on assumptions about demographic and economic trends that serve as inputs for models of Social Security. These results confirm the inability of current Social Security rules to ease the actuarial deficit.
Este trabalho tem a finalidade de construir modelos, utilizando tÃcnicas da econometria, que expliquem os componentes do saldo da PrevidÃncia Social, ou seja, o valor da arrecadaÃÃo lÃquida e o valor dos benefÃcios do Regime Geral da PrevidÃncia Social. Esses modelos foram submetidos Ãs validaÃÃes estatÃsticas indicadas em referencial teÃrico de econometria para aplicaÃÃo do mÃtodo dos mÃnimos quadrados ordinÃrios do modelo de regressÃo linear clÃssico. Com o aumento da longevidade populacional brasileira e a diminuiÃÃo gradativa da natalidade, formando uma quase conspiraÃÃo biomÃtrica, aliada Ãs regras benevolentes de concessÃo de benefÃcios, as projeÃÃes revelam que as despesas previdenciÃrias continuarÃo acima das receitas e a diferenÃa entre os dois irà aumentar ano apÃs ano. Estas projeÃÃes, no entanto, dependem de hipÃteses sobre tendÃncias demogrÃficas e econÃmicas, que servem como insumos para modelos da Seguridade Social. Os resultados ratificam a incapacidade das atuais regras da PrevidÃncia Social de amenizar o dÃficit atuarial.
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Bajracharya, Dinesh. "Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20400.

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Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales.
Program: Magisterutbildning i informatik
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Ayvatoglu, Dimitris 1976. "Econometric modeling of ocean freight rates : the case of tankers." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/91340.

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Adedeji, Olumuyiwa Samson. "The intertemporal approach to modeling the current account : evidence from Nigeria." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38142.

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This dissertation has two objectives. The first is to modify the existing Present Value Model of the Current Account (PVMCA) to reflect the major features of the Nigerian economy and to determine if this resulting theoretical framework is valid for the analysis of the Nigerian current account for the period 1960--97. The second objective is to examine the excessiveness and sustainability of the Nigerian current account deficits during this period.
To achieve these objectives, the thesis presents a model of current account determination that is based upon the permanent-income hypothesis of private consumption behavior. We derive a present value relationship among the current account, expected changes in net output and a consumption-based real interest rate. This thesis then extends this framework to incorporate changes in the terms of trade and possible asymmetric access to the international financial markets. It also conducts an empirical estimation of the several variants of the PVMCA. The econometric results show that an intertemporal model of current account determination that includes changes in the interest rate, exchange rate and terms of trade outperforms one that excludes them.
This thesis represents the first attempt to use an intertemporal model of the current account and selected macroeconomic and structural indicators to assess the external position of the Nigerian economy. The empirical results support the hypothesis that current account deficits accompanied by macroeconomic instability and structural weaknesses can generate an external crisis.
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Desaling, Germay Meron. "Modeling and Forecasting Unemployment Rate In Sweden using various Econometric Measures." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-51425.

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Books on the topic "Econometric modeling"

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Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".
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International Symposium in Economic Theory and Econometrics (3rd 1986 Austin, Tex.). Dynamic econometric modeling. Cambridge [Cambridgeshire]: Cambridge University Press, 1988.

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Marco, Bee, ed. Econometric modeling perspectives. New York: Nova Science Publishers, 2008.

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Perryman, M. Ray, and James R. Schmidt, eds. Regional Econometric Modeling. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3267-8.

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1952-, Perryman M. Ray, and Schmidt James R, eds. Regional econometric modeling. Boston: Klumer/Nijhoff, 1986.

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Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson, and Viktor Polyak. Econometric modeling in the GRETL package. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.

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The tutorial describes the capabilities of the GRETL statistical package for computer data analysis and econometric modeling based on spatial data and time series. Using concrete economic examples, GRETL considers classical and generalized models of linear and nonlinear regression, methods for detecting and eliminating multicollinearity, models with variable structure, autoregressive processes, methods for testing and eliminating autocorrelation, as well as discrete choice models and systems of simultaneous equations. For the convenience of users, the tutorial contains all the task data files used in the work in the format .GDTs are collected in an application in the cloud so that users have access to them. Meets the requirements of the federal state educational standards of higher education of the latest generation in the disciplines of "Econometrics" and "Econometric modeling". For students and teachers of economic universities in the field of Economics, as well as researchers who use econometric methods to model socio-economic phenomena and processes.
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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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Logit modeling: Practical applications. Newbury Park: Sage Publications, 1992.

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E, Becker William, and Walstad William B, eds. Econometric modeling in economic education research. Boston, Mass: Kluwer-Nijhoff Pub., 1987.

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Becker, William E., and William B. Walstad, eds. Econometric Modeling in Economic Education Research. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3271-5.

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Book chapters on the topic "Econometric modeling"

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Grant, Darren. "Econometric Modeling." In Springer Texts in Business and Economics, 109–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01734-7_8.

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Winkelmann, Rainer. "Econometric Modeling — Extensions." In Econometric Analysis of Count Data, 113–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24728-9_4.

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Winkelmann, Rainer. "Econometric Modeling — Extensions." In Lecture Notes in Economics and Mathematical Systems, 110–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-21735-1_4.

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Winkelmann, Rainer. "Econometric Modeling — Extensions." In Econometric Analysis of Count Data, 119–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04149-9_4.

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Winkelmann, Rainer. "Econometric Modeling — Extensions." In Econometric Analysis of Count Data, 89–125. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03465-1_4.

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Mahoney, Daniel. "Econometric Concepts." In Modeling and Valuation of Energy Structures, 191–271. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137560155_6.

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Winkelmann, Rainer. "Econometric Modeling — Basic Issues." In Econometric Analysis of Count Data, 61–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24728-9_3.

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Winkelmann, Rainer. "Econometric Modeling — Basic Issues." In Lecture Notes in Economics and Mathematical Systems, 64–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-21735-1_3.

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Winkelmann, Rainer. "Econometric Modeling — Basic Issues." In Econometric Analysis of Count Data, 65–118. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04149-9_3.

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Winkelmann, Rainer. "Econometric Modeling — Basic Issues." In Econometric Analysis of Count Data, 53–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03465-1_3.

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Conference papers on the topic "Econometric modeling"

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Zaychikova, N. A. "Econometric Modeling Of External Migration." In GCPMED 2018 - International Scientific Conference "Global Challenges and Prospects of the Modern Economic Development. Cognitive-Crcs, 2019. http://dx.doi.org/10.15405/epsbs.2019.03.110.

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Sun, Haitao. "The Computer Implementation of Econometric Model." In 2nd International Conference on Computer Application and System Modeling. Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccasm.2012.160.

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Zhong, Ming, and Qin Shi. "Econometric Modeling of Second-hand Tanker Prices." In 2007 IEEE International Conference on Automation and Logistics. IEEE, 2007. http://dx.doi.org/10.1109/ical.2007.4338656.

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Seabold, Skipper, and Josef Perktold. "Statsmodels: Econometric and Statistical Modeling with Python." In Python in Science Conference. SciPy, 2010. http://dx.doi.org/10.25080/majora-92bf1922-011.

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Sinyavskaya, Tatiana. "ECONOMETRIC MODELING OF MICRO-ECONOMIC DEMAND FOR INSURANCE." In 5th International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgemsocial2018/1.3/s03.017.

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Klimashina, Vasilisa Vyacheslavovna. "Econometric modeling of share quotation in PJCS "Rostelekom"." In III International applied research conference, Chair Tatiana Mikhailovna Tikhomirova. TSNS Interaktiv Plus, 2016. http://dx.doi.org/10.21661/r-114097.

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Arzhenovskiy, Sergey. "ECONOMETRIC MODELING OF INVESTMENT RISK IN THE ART MARKET." In 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. Stef92 Technology, 2015. http://dx.doi.org/10.5593/sgemsocial2015/b22/s6.019.

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Ma, Si, and Qingfeng Zhou. "Research on China Railway Freight Econometric Analysis and Modeling." In Inernational Conference of Logistics Engineering and Management 2012. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412602.0072.

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Bogomolov, A. B., V. P. Nevezhin, and L. O. Chagovets. "Using Econometric Modeling in Likelihood Assessing of Investment Activity Risks." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516898.

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Rubilar Torrealba, Rolando, Karime Chahuán Jiménez, and Hanns De La Fuente-Mella. "Econometric Modeling for the Management and Decomposition of Financial Risk." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001444.

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This research presents a methodological analysis that will allow to actively manage the risk of financial assets, through an understandable study and mix of technical differences used by the financial literature. In this way, the research will allow the delivery of precise information on the risk-generating components of the assets studied. The methodology used corresponds to the wavelet decomposition method, combined with the VaR methodology, which as a whole proves to be an efficient way of controlling the financial risk of the investment portfolios used, thus allowing to identify the main risk generating components to which it is applied. investors and fund managers submit.
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Reports on the topic "Econometric modeling"

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Fair, Ray, and Robert Shiller. Econometric Modeling as Information Aggregation. Cambridge, MA: National Bureau of Economic Research, May 1987. http://dx.doi.org/10.3386/w2233.

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Neely, Christopher J., Sébastien Laurent, and Deniz Erdemlioglu. Econometric Modeling of Exchange Rate Volatility and Jumps. Federal Reserve Bank of St. Louis, 2012. http://dx.doi.org/10.20955/wp.2012.008.

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Rosen, Adam, and Andrew Chesher. Econometric Modeling of Interdependent Discrete Choice with Applications to Market Structure. The IFS, June 2020. http://dx.doi.org/10.1920/wp.cem.2020.2520.

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Hlushak, Oksana M., Svetlana O. Semenyaka, Volodymyr V. Proshkin, Stanislav V. Sapozhnykov, and Oksana S. Lytvyn. The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects). [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3860.

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This article demonstrates that mathematics in the system of higher education has outgrown the status of the general education subject and should become an integral part of the professional training of future bachelors, including economists, on the basis of intersubject connection with special subjects. Such aspects as the importance of improving the scientific and methodological support of mathematical training of students by means of digital technologies are revealed. It is specified that in order to implement the task of qualified training of students learning econometrics and economic and mathematical modeling, it is necessary to use digital technologies in two directions: for the organization of electronic educational space and in the process of solving applied problems at the junction of the branches of economics and mathematics. The advantages of using e-learning courses in the educational process are presented (such as providing individualization of the educational process in accordance with the needs, characteristics and capabilities of students; improving the quality and efficiency of the educational process; ensuring systematic monitoring of the educational quality). The unified structures of “Econometrics”, “Economic and mathematical modeling” based on the Moodle platform are the following ones. The article presents the results of the pedagogical experiment on the attitude of students to the use of e-learning course (ELC) in the educational process of Borys Grinchenko Kyiv University and Alfred Nobel University (Dnipro city). We found that the following metrics need improvement: availability of time-appropriate mathematical materials; individual approach in training; students’ self-expression and the development of their creativity in the e-learning process. The following opportunities are brought to light the possibilities of digital technologies for the construction and research of econometric models (based on the problem of dependence of the level of the Ukrainian population employment). Various stages of building and testing of the econometric model are characterized: identification of variables, specification of the model, parameterization and verification of the statistical significance of the obtained results.
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Ismail, Zenobia. Interaction Between Food Prices and Political Instability. Institute of Development Studies (IDS), May 2021. http://dx.doi.org/10.19088/k4d.2021.091.

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This report reviews the literature on links between international food prices and political instability (including protests, riots and social unrest). The literature on food prices and protests, riots, unrest, or violent incidents consists mainly of peer-reviewed scholarly articles that utilise econometric modeling. Some early studies examined the links between international food prices and political instability and found conflicting results. Some assessments concluded that there were links between international food prices or food insecurity and the number of violent incidents, while others found that such a link was tenuous. This literature review covers some of the main arguments and findings in the recent literature on food prices and political instability or conflict. The majority of the econometric studies in this review find that there is a link between food price increases and a greater probability of protests, riots or social unrest. However, there are still a few studies that have contradictory results. So, the debate on the effect of food prices on political stability continues. Food subsidies, cash transfers, price controls, and the elimination of trade barriers are some of the policy interventions that may address rising food prices and mitigate the rise of violent collective action. However, the literature questions the effectiveness of such policies in cases where violence or protest action stems from deeper, underlying economic or political grievances.
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