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Books on the topic 'Econometric modeling'

To see the other types of publications on this topic, follow the link: Econometric modeling.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

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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2

International Symposium in Economic Theory and Econometrics (3rd 1986 Austin, Tex.). Dynamic econometric modeling. Cambridge [Cambridgeshire]: Cambridge University Press, 1988.

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3

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

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4

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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5

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

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6

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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7

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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8

Logit modeling: Practical applications. Newbury Park: Sage Publications, 1992.

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9

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

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10

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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11

Kreinovich, Vladik, and Songsak Sriboonchitta, eds. Structural Changes and their Econometric Modeling. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04263-9.

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12

Angelidis, Timotheos. Econometric modeling of value at risk. Hauppauge, NY: Nova Science Publishers, 2009.

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13

Kaufmann, Sylvia. Modeling credit aggregates. [Vienna:]: Oesterreichische Nationalbank, 2004.

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14

Christiano, Lawrence J. Modeling money. Cambridge, MA: National Bureau of Economic Research, 1998.

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15

Acemoglu, Daron. Modeling inefficient institutions. Cambridge, Mass: National Bureau of Economic Research, 2006.

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16

Acemoglu, Daron. Modeling inefficient institutions. Cambridge, MA: Massachusetts Institute of Technology, Dept. of Economics, 2005.

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17

Onatski, Alexei. Modeling model uncertainty. Cambridge, Mass: National Bureau of Economic Research, 2003.

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18

Sturton, Mark. Modeling the Fiji economy. Honolulu, Hawaii: Pacific Islands Development Program, 1989.

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19

Florens, J. P. Econometric: Modeling and inference = Économétrie : modélisation et inférence. Cambridge: Cambridge University Press, 2007.

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20

Kolpakov, Vasiliy. Economic and mathematical and econometric modeling: Computer workshop. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/24417.

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The textbook presents mathematical research methods and models of economic objects and processes designed for the analysis and prediction of economic factors and develop control solutions as in the deterministic conditions, and in conditions of some uncertainty, and dynamics. Each Chapter of the book consists of a theoretical framework, discussed in detail several examples and tasks for independent work. As workbench simulation uses standard office the program Excel and Mathcad. Tutorial focused on independent performance of students individual tasks on disciplines "Economic-mathematical methods" and "Econometrics". Meets the requirements of Federal state educational standard of higher education of the last generation. The publication is intended for students and postgraduate students in economic disciplines. It can also be useful as they perform final qualifying works. The book will be useful for practitioners engaged in the analysis of the current financial and economic condition and future development of firms and businesses.

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21

Empirical modeling in economics: Specification and evaluation. Cambridge: Cambridge University Press, 1999.

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22

Empirical vector autoregressive modeling. Berlin: Springer-Verlag, 1994.

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23

Roth, Alvin E. Two-sided matching: A study in game-theoretic modeling and analysis. Cambridge [England]: Cambridge University Press, 1990.

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24

Stander, Yolanda S. Yield curve modeling. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2005.

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25

A, Barnett William, and Gallant A. Ronald 1942-, eds. New approaches to modeling, specification selection, and econometric inference: Proceedings of the First International Symposium in Economic Theory and Econometrics. Cambridge [England]: Cambridge University Press, 1989.

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26

Chipika, Jesimen. Macroeconomic modeling and forecasting manual. Harare, Zimbabwe: Macroeconomic and Financial Management Institute of Eastern and Southern Africa, 2012.

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27

Fuhrer, Jeffrey C. Modeling long-term nominal interest rates. Boston, Mass: Federal Reserve Bank of Boston, 1995.

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28

Fuhrer, Jeffrey C. Modeling long-term nominal interest rates. Boston, Mass: Federal Reserve Bank of Boston, 1995.

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29

Fuhrer, Jeffrey C. Modeling long-term nominal interest rates. Boston, Mass: Federal Reserve Bank of Boston, 1995.

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30

Christensen, Bent J. Economic modeling and inference. Princeton: Princeton University Press, 2009.

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31

Donaldson, Glen. Volatility forecasts, trading volume, and the ARCH versus option-implied volatility trade-off. [Atlanta]: Federal Reserve Bank of Atlanta, 2004.

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32

Modeling mineral and energy markets. Boston, Mass: Kluwer Academic, 1999.

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33

Khan, Aubhik. Modeling inventories over the business cycle. [Minneapolis, Minn.]: Federal Reserve Bank of Minneapolis, 2004.

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34

Ravallion, Martin. Issues in measuring and modeling poverty. Washington, DC: World Bank, Policy Research Dept., Poverty and Human Resources Division, 1996.

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35

Toshitaka, Sekine. Modeling and forecasting inflation in Japan. [Washington, D.C.]: International Monetary Fund, Policy Development and Review Department, 2001.

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36

Khan, Aubhik. Modeling inventories over the business cycle. Cambridge, MA: National Bureau of Economic Research, 2004.

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37

M, Roberts John. Modeling aggregate investment: A fundamentalist approach. Washington, D.C: Federal Reserve Board, 2003.

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38

Khan, Aubhik. Modeling inventories over the business cycle. Philadelphia, PA: Federal Reserve Bank of Philadelphia, 2004.

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39

Callen, Tim. Modeling and forecasting inflation in India. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Department, 1999.

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40

Fontnouvelle, Patrick de. Implication of alternative operational risk modeling techniques. Cambridge, Mass: National Bureau of Economic Research, 2005.

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41

Probability theory and statistical inference: Econometric modeling with observational data. Cambridge, UK: Cambridge University Press, 1999.

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42

Jorgenson, Dale W. Econometrics: Econometric Modeling of Producer Behavior. MIT Press, 2000.

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43

Financial Econometric Modeling. Oxford University Press, Incorporated, 2020.

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44

Regional Econometric Modeling. Springer, 2012.

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45

Barnett, William A., Ernst R. Berndt, and Halbert White, eds. Dynamic Econometric Modeling. Cambridge University Press, 1988. http://dx.doi.org/10.1017/cbo9780511664342.

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46

Phillips, Peter C. B., Stan Hurn, Jun Yu, and Vance L. Martin. Financial Econometric Modeling. Oxford University Press, Incorporated, 2020.

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47

Schmidt, James R., and M. Ray Perryman. Regional Econometric Modeling. Springer London, Limited, 2012.

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48

Schmidt, James R., and M. Ray Perryman. Regional Econometric Modeling. Springer Netherlands, 2011.

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49

Hendry, David F., Bent Nielsen, and David F. F. Hendry. Econometric Modeling: A Likelihood Approach. Princeton University Press, 2012.

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50

Econometric Modeling: A Likelihood Approach. Princeton University Press, 2007.

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