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Journal articles on the topic 'Simultaneous equation models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Tang, Hou Xing. "Identification of Simultaneous Equation Model in Econometrics." Applied Mechanics and Materials 58-60 (June 2011): 1944–47. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1944.

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Identification is an important topic on simultaneous equation econometric model. It determines that whether the coefficients of the simultaneous equation models can be estimated. From the definition of identification perspective, this article will provide a new method about identification based on the relation between identified equations and the linear combinations of all equations.

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2

Anderson, Theodore W. "ADMISSIBLE SIGNIFICANCE TESTS IN SIMULTANEOUS EQUATION MODELS." Econometric Theory 33, no. 3 (April 21, 2017): 534–50. http://dx.doi.org/10.1017/s0266466616000542.

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Consider testing the null hypothesis that a single structural equation has specified coefficients. The alternative hypothesis is that the relevant part of the reduced form matrix has proper rank, that is, that the equation is identified. The usual linear model with normal disturbances is invariant with respect to linear transformations of the endogenous and of the exogenous variables. When the disturbance covariance matrix is known, it can be set to the identity, and the invariance of the endogenous variables is with respect to orthogonal transformations. The likelihood ratio test is invariant with respect to these transformations and is the best invariant test. Furthermore it is admissible in the class of all tests. Any other test has lower power and/or higher significance level. In particular, this likelihood ratio test dominates a test based on the Two-Stage Least Squares estimator.

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3

Halkos, George E., and Kyriaki D. Tsilika. "Programming Identification Criteria in Simultaneous Equation Models." Computational Economics 46, no. 1 (April 29, 2014): 157–70. http://dx.doi.org/10.1007/s10614-014-9444-9.

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4

Hernández-Sanjaime, Rocío, Martín González, and Jose J. López-Espín. "Estimation of Multilevel Simultaneous Equation Models through Genetic Algorithms." Mathematics 8, no. 12 (November 24, 2020): 2098. http://dx.doi.org/10.3390/math8122098.

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Problems in estimating simultaneous equation models when error terms are not intertemporally uncorrelated has motivated the introduction of a new multivariate model referred to as Multilevel Simultaneous Equation Model (MSEM). The maximum likelihood estimation of the parameters of an MSEM has been set forth. Because of the difficulties associated with the solution of the system of likelihood equations, the maximum likelihood estimator cannot be obtained through exhaustive search procedures. A hybrid metaheuristic that combines a genetic algorithm and an optimization method has been developed to overcome both technical and analytical limitations in the general case when the covariance structure is unknown. The behaviour of the hybrid metaheuristic has been discussed by varying different tuning parameters. A simulation study has been included to evaluate the adequacy of this estimator when error terms are not serially independent. Finally, the performance of this estimation approach has been compared with regard to other alternatives.

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5

Shina, Arya Fendha Ibnu. "ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB." MEDIA STATISTIKA 11, no. 2 (December 30, 2018): 79–91. http://dx.doi.org/10.14710/medstat.11.2.79-91.

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Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations

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6

Hepple, Leslie W. "Testing for spatial autocorrelation in simultaneous equation models." Computers, Environment and Urban Systems 21, no. 5 (September 1997): 307–15. http://dx.doi.org/10.1016/s0198-9715(98)00002-7.

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7

Ristl, Robin, Ludwig Hothorn, Christian Ritz, and Martin Posch. "Simultaneous inference for multiple marginal generalized estimating equation models." Statistical Methods in Medical Research 29, no. 6 (September 17, 2019): 1746–62. http://dx.doi.org/10.1177/0962280219873005.

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Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.

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8

Polovina, Jeffrey J. "A System of Simultaneous Dynamic Production and Forecast Models for Mulispecies or Multiarea Applications." Canadian Journal of Fisheries and Aquatic Sciences 46, no. 6 (June 1, 1989): 961–63. http://dx.doi.org/10.1139/f89-124.

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A system of simultaneous dynamic production models is presented for multispecies or multiarea applications. The system of equations arises by using a linear dynamic production model with one equation for each area or species. The parameter estimation for the system of equations is particularly efficient when some of the parameters are assumed to have the same value in more than one equation.

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9

Bartus, Tamás. "Multilevel Multiprocess Modeling with Gsem." Stata Journal: Promoting communications on statistics and Stata 17, no. 2 (June 2017): 442–61. http://dx.doi.org/10.1177/1536867x1701700211.

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Multilevel multiprocess models are simultaneous equation systems that include multilevel hazard equations with correlated random effects. Demographers routinely use these models to adjust estimates for endogeneity and sample selection. In this article, I demonstrate how multilevel multiprocess models can be fit with the gsem command. I distinguish between two classes of multilevel multiprocess models: nonrecursive systems of hazard equations without observed endogenous variables and recursive systems that include a hazard equation with observed endogenous qualitative variables. I illustrate the estimation of both classes of models using sample datasets shipped with the statistical software aML. I pay special attention to identifying structural coefficients in nonrecursive simultaneous systems.

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10

Hsiao, Cheng, and Qiankun Zhou. "JIVE FOR PANEL DYNAMIC SIMULTANEOUS EQUATIONS MODELS." Econometric Theory 34, no. 6 (November 2, 2017): 1325–69. http://dx.doi.org/10.1017/s0266466617000421.

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We consider the method of moments estimation of a structural equation in a panel dynamic simultaneous equations model under different sample size combinations of cross-sectional dimension, N, and time series dimension, T. Two types of linear transformation to remove the individual-specific effects from the model, first difference and forward orthogonal demeaning, are considered. We show that the Alvarez and Arellano (2003) type GMM estimator under both transformations is consistent only if ${T \over N} \to 0$ as $\left( {N,T} \right) \to \infty $. However, it is asymptotically biased if ${{{T^3}} \over N} \to \kappa \ne 0 < \infty$ as $\left( {N,T} \right) \to \infty $. Since the validity of statistical inference depends critically on whether an estimator is asymptotically unbiased, we suggest a jackknife bias reduction method and derive its limiting distribution. Monte Carlo studies are conducted to demonstrate the importance of using an asymptotically unbiased estimator to obtain valid statistical inference.

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11

Brown, Bryan W. "The Identification Problem in Simultaneous Equation Models with Identities." International Economic Review 26, no. 1 (February 1985): 45. http://dx.doi.org/10.2307/2526526.

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12

Calzolari, Giorgio. "Forecast Variance in Dynamic Simulation of Simultaneous Equation Models." Econometrica 55, no. 6 (November 1987): 1473. http://dx.doi.org/10.2307/1913569.

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13

Streibel, Mariane, and Andrew Harvey. "Estimation of simultaneous equation models with stochastic trend components." Journal of Economic Dynamics and Control 17, no. 1-2 (January 1993): 263–87. http://dx.doi.org/10.1016/s0165-1889(06)80012-6.

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14

Hahn, Jinyong, and Jerry Hausman. "Notes on bias in estimators for simultaneous equation models." Economics Letters 75, no. 2 (April 2002): 237–41. http://dx.doi.org/10.1016/s0165-1765(01)00602-4.

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15

Anderson, T. W., and Naoto Kunitomo. "Tests of overidentification and predeterminedness in simultaneous equation models." Journal of Econometrics 54, no. 1-3 (October 1992): 49–78. http://dx.doi.org/10.1016/0304-4076(92)90099-d.

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16

Zaidi, Saida, and Zouari Ghorbel. "Causal interactions between economic growth and electricity consumption in 46 African countries: Dynamic Simultaneous Equation Models." International Academic Journal of Economics 05, no. 02 (June 28, 2018): 55–66. http://dx.doi.org/10.9756/iaje/v5i2/1810015.

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17

EL-Sheikh, Ahmed Amin, and Amt-allateef Mohammed Ali Al-Hamzi. "Review: estimation of the simultaneous equation models with measurement error." International Journal of Contemporary Mathematical Sciences 9 (2014): 331–48. http://dx.doi.org/10.12988/ijcms.2014.4436.

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18

Lee, Lung-Fei. "Semiparametric Estimation of Simultaneous-Equation Microeconometric Models with Index Restrictions." Japanese Economic Review 49, no. 4 (December 1998): 343–80. http://dx.doi.org/10.1111/1468-5876.00090.

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19

Lee, Lung-Fei. "Semiparametric instrumental variable estimation of simultaneous equation sample selection models." Journal of Econometrics 63, no. 2 (August 1994): 341–88. http://dx.doi.org/10.1016/0304-4076(93)01571-3.

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20

Supriyanto, Supriyanto. "Analysis of Social, Economic and Population in Central Java Using the Dynamic Data Panel Simultaneous Equation Model." JSSH (Jurnal Sains Sosial dan Humaniora) 3, no. 1 (August 13, 2019): 79. http://dx.doi.org/10.30595/jssh.v3i1.4309.

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A single equation model that is often used ignores the interdependence between response variables. Frequently encountered variables that have a two-way relationship. These interrelated two-way relationships can be summarized in a simultaneous equation model system. There is a relationship between variables which are in fact dynamic. In the system model of simultaneous equations with dynamic panel data, each structural equation is a dynamic panel data regression equation. The estimation of using Ordinary Least Square (OLS) in the dynamic panel data model results in biased and inconsistent predictors because there is a lag of the dependent variable that correlates with the error. First difference in dynamic panel models is used to eliminate individual effects. Instrument variables are needed, namely variables that do not correlate with errors. Therefore, dynamic panel data models are more suitable to be used in analyzing poverty and social change. From the simultaneous equation model obtained, the dominant factors affecting the level of poverty in Central Java Province are the unemployment rate, Human Development Index, labor force participation rate, population, and Gross Regional Domestic Product.

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21

Blundell, Richard, Dennis Kristensen, and Rosa L. Matzkin. "Control Functions and Simultaneous Equations Methods." American Economic Review 103, no. 3 (May 1, 2013): 563–69. http://dx.doi.org/10.1257/aer.103.3.563.

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The control function approach is a convenient method of estimation in simultaneous equation systems. This requires that the system can be expressed in triangular form with variables satisfying a conditional mean independence restriction. Linear simultaneous models with additive errors can always be expressed in this form. However, in nonlinear nonadditive simultaneous systems, conditional independence requires a strong additional restriction known as control function separability. We argue that nonadditive models are a key characteristic of simultaneous models of economic behavior with unobserved heterogeneity. We review alternative “system” approaches and document the biases that occur when the control function approach is used inappropriately.

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22

Blundell, R. W., and R. J. Smith. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models." Review of Economic Studies 56, no. 1 (January 1, 1989): 37–57. http://dx.doi.org/10.2307/2297748.

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23

Hausman, Jerry A., Whitney K. Newey, and William E. Taylor. "Efficient Estimation and Identification of Simultaneous Equation Models with Covariance Restrictions." Econometrica 55, no. 4 (July 1987): 849. http://dx.doi.org/10.2307/1911032.

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24

Capps, Oral, and William D. Grubbs. "A monte carlo study of collinearity in linear simultaneous equation models∗." Journal of Statistical Computation and Simulation 39, no. 3 (June 1991): 139–62. http://dx.doi.org/10.1080/00949659108811346.

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25

Merckens, A., and P. A. Bekker. "Identification of simultaneous equation models with measurement error: a computerized evaluation." Statistica Neerlandica 47, no. 4 (December 1993): 233–44. http://dx.doi.org/10.1111/j.1467-9574.1993.tb01420.x.

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26

So, Sun-Ha, You-Sung Park, and Dong-Hee Lee. "A Test of the Rank Conditions in the Simultaneous Equation Models." Communications for Statistical Applications and Methods 16, no. 1 (January 31, 2009): 115–25. http://dx.doi.org/10.5351/ckss.2009.16.1.115.

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27

Jedidi, Kamel, Venkatram Ramaswamy, Wayne S. DeSarbo, and Michel Wedel. "On estimating finite mixtures of multivariate regression and simultaneous equation models." Structural Equation Modeling: A Multidisciplinary Journal 3, no. 3 (January 1996): 266–89. http://dx.doi.org/10.1080/10705519609540044.

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28

Rhoads, Bruce L. "Multicollinearity and Parameter Estimation in Simultaneous-Equation Models of Fluvial Systems." Geographical Analysis 23, no. 4 (September 3, 2010): 346–61. http://dx.doi.org/10.1111/j.1538-4632.1991.tb00244.x.

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29

Wadud, Zia. "Decomposing the drivers of aviation fuel demand using simultaneous equation models." Energy 83 (April 2015): 551–59. http://dx.doi.org/10.1016/j.energy.2015.02.061.

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30

Puelz, Charles, Zach Danial, Jay S. Raval, Jonathan L. Marinaro, Boyce E. Griffith, and Charles S. Peskin. "Models for plasma kinetics during simultaneous therapeutic plasma exchange and extracorporeal membrane oxygenation." Mathematical Medicine and Biology: A Journal of the IMA 38, no. 2 (February 25, 2021): 255–71. http://dx.doi.org/10.1093/imammb/dqab003.

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Abstract This paper focuses on the derivation and simulation of mathematical models describing new plasma fraction in blood for patients undergoing simultaneous extracorporeal membrane oxygenation and therapeutic plasma exchange. Models for plasma exchange with either veno-arterial or veno-venous extracorporeal membrane oxygenation are considered. Two classes of models are derived for each case, one in the form of an algebraic delay equation and another in the form of a system of delay differential equations. In special cases, our models reduce to single compartment ones for plasma exchange that have been validated with experimental data (Randerson et al., 1982, Artif. Organs, 6, 43–49). We also show that the algebraic differential equations are forward Euler discretizations of the delay differential equations, with timesteps equal to transit times through model compartments. Numerical simulations are performed to compare different model types, to investigate the impact of plasma device port switching on the efficiency of the exchange process, and to study the sensitivity of the models to their parameters.

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31

Kim, Kyung Hwan, and Byung Ho Park. "Simultaneous Equation Models for Evaluating Roundabout Accidents According to Different Driving Types." Journal of Korean Society of Transportation 30, no. 5 (October 31, 2012): 3–10. http://dx.doi.org/10.7470/jkst.2012.30.5.003.

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32

López-Espín, Jose J., and Domingo Giménez. "Obtaining simultaneous equation models from a set of variables through genetic algorithms." Procedia Computer Science 1, no. 1 (May 2010): 427–35. http://dx.doi.org/10.1016/j.procs.2010.04.046.

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33

Kiviet, Jan F., and Garry D. A. Phillips. "The bias of the ordinary least squares estimator in simultaneous equation models." Economics Letters 53, no. 2 (November 1996): 161–67. http://dx.doi.org/10.1016/s0165-1765(96)00908-1.

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34

Lye, J. N. "Finite-sample properties of limited-iinformation estimators in misspecified simultaneous equation models." Journal of Statistical Computation and Simulation 39, no. 4 (September 1991): 241–57. http://dx.doi.org/10.1080/00949659108811359.

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35

Sapra, Sunil K. "INSTRUMENTAL VARIABLE ESTIMATION IN NONLINEAR SIMULTANEOUS EQUATION MODELS WITH LIMITED DEPENDENT VARIABLES." Bulletin of Economic Research 41, no. 4 (October 1989): 275–86. http://dx.doi.org/10.1111/j.1467-8586.1989.tb00343.x.

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36

Phillips, Garry D. A., and Gareth Liu-Evans. "Approximating and reducing bias in 2SLS estimation of dynamic simultaneous equation models." Computational Statistics & Data Analysis 100 (August 2016): 734–62. http://dx.doi.org/10.1016/j.csda.2015.11.011.

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37

Huang, Shongming, and Stephen J. Titus. "Estimating a system of nonlinear simultaneous individual tree models for white spruce in boreal mixed-species stands." Canadian Journal of Forest Research 29, no. 11 (December 1, 1999): 1805–11. http://dx.doi.org/10.1139/x99-139.

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A system of three interdependent, tree-level nonlinear equations was fitted. The system was used in an individual tree simulator to predict total tree height, periodic tree diameter increment, and height increment for white spruce (Picea glauca (Moench) Voss) grown in boreal mixed-species stands in Alberta. Because the variables appeared on the left-hand side of the equations also appeared on the right-hand side of the equations in the system, the system was estimated using nonlinear simultaneous techniques. Testing of cross-equation correlations using the Breusch and Pagan statistic indicated that the error terms of the related equations in the system are significantly correlated, suggesting that the parameter estimates obtained from simultaneous techniques are consistent and asymptotically more efficient than those obtained from ordinary least squares procedures applied to individual equations of the system.

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38

Archer, Keith. "A Simultaneous Equation Model of Canadian Voting Behaviour." Canadian Journal of Political Science 20, no. 3 (September 1987): 553–72. http://dx.doi.org/10.1017/s0008423900049921.

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AbstractRecent research on voting in United States presidential elections has begun to disentangle long-term and short-term components of electoral choice. This has been achieved through the use of complex models involving instrumental variables and two-stage or three-stage least squares regression. These techniques are particularly appropriate to understanding the voting decision in Canada because of the short-term variability of partisan identifications. Modelling voting choice, using simultaneous equations and data from the 1979 Canadian National Election Study, it was found that the major attitudinal determinants of voting—party identification and attitudes towards issues and party leaders—were strongly related to one another and to the direction of vote whereas sociodemographic characteristics were only weakly related to political attitudes and behaviour. In addition, the strength of these variables may vary across the major parties in any given election.

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39

Kumar, Ajith, and William R. Dillon. "The Interaction of Measurement and Structure in Simultaneous Equation Models with Unobservable Variables." Journal of Marketing Research 24, no. 1 (February 1987): 98. http://dx.doi.org/10.2307/3151757.

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40

Olmeda, Natividad Guadalajara, and Isabel Barrachina Martinez. "Application of simultaneous equation models to temporary disability prescriptions in primary healthcare centres." International Journal of Computer Mathematics 91, no. 2 (July 2013): 252–60. http://dx.doi.org/10.1080/00207160.2013.808334.

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41

Iglesias, Emma M., and Garry D. A. Phillips. "Almost Unbiased Estimation in Simultaneous Equation Models With Strong and/or Weak Instruments." Journal of Business & Economic Statistics 30, no. 4 (October 2012): 505–20. http://dx.doi.org/10.1080/07350015.2012.715959.

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42

Phillips, Garry D. A. "An alternative approach to obtaining Nagar-type moment approximations in simultaneous equation models." Journal of Econometrics 97, no. 2 (August 2000): 345–64. http://dx.doi.org/10.1016/s0304-4076(99)00075-5.

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43

Cai, Lixin. "Estimation of simultaneous equation models with latent dependent variables: a Monte Carlo evaluation." Applied Economics Letters 16, no. 11 (July 2009): 1107–12. http://dx.doi.org/10.1080/13504850701335368.

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44

Zellner, Arnold, Luc Bauwens, and Herman K. Van Dijk. "Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods." Journal of Econometrics 38, no. 1-2 (May 1988): 39–72. http://dx.doi.org/10.1016/0304-4076(88)90026-7.

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45

Nowak, Eugen. "Identification of simultaneous equation models with measurement errors based on time series structure." Journal of Econometrics 40, no. 2 (February 1989): 319–25. http://dx.doi.org/10.1016/0304-4076(89)90088-2.

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46

Kumar, Ajith, and William R. Dillon. "The Interaction of Measurement and Structure in Simultaneous Equation Models with Unobservable Variables." Journal of Marketing Research 24, no. 1 (February 1987): 98–105. http://dx.doi.org/10.1177/002224378702400109.

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The authors report the results of some theoretical investigations of interactions that occur in the simultaneous estimation of parameters. Under the assumptions of a perfectly specified model for a set of non-overlapping indicators and no sampling fluctuations, an overall consistency criterion is developed showing that though measurement and structure can be evaluated independently of each other, in general, they should not be. The proposed consistency criterion clearly shows that the internal and external consistency criteria prescribed by Anderson and Gerbing are not sufficient; that is, there are correlation matrices for which the criteria of internal and external consistency both can be satisfied, yet the matrix as a whole is inconsistent. Implications of the interaction between measurement and structure are also discussed.

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47

Hernández-Sanjaime, Rocío, Martín González, Antonio Peñalver, and Jose J. López-Espín. "Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm." Entropy 23, no. 4 (March 24, 2021): 384. http://dx.doi.org/10.3390/e23040384.

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The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

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48

Checchi, Daniele, and Luca Nunziata. "Models of unionism and unemployment." European Journal of Industrial Relations 17, no. 2 (June 2011): 141–52. http://dx.doi.org/10.1177/0959680111400896.

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We investigate the problem of simultaneous determination of labour market institutions and outcomes in single equation multi-country estimations by presenting an empirical analysis of unemployment and union density in 20 OECD countries. When explicitly modelling potential endogeneity and heterogeneity, our results suggest that unions contribute to explaining unemployment in different ways than previously thought. In addition, the relationship between unemployment and union density is heterogeneous across countries, depending on the way in which income support for the unemployed is organized.

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49

Chipman, John S. "HAAVELMO’S CONTRIBUTIONS TO SIMULTANEOUS-EQUATIONS ESTIMATION." Econometric Theory 31, no. 2 (July 31, 2014): 233–48. http://dx.doi.org/10.1017/s0266466614000280.

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This paper surveys Trygve Haavelmo’s contributions to econometrics. A brief summary of his 1944 monograph is followed by an analysis of the six important papers he contributed during 1943–47. Four of them were devoted to macroeconomic models including estimation of the Keynesian marginal propensity to consume; the first two introduced the methodology of a system of structural equations; the third marked a milestone in econometric method by deriving the reduced form of such a system; and the fourth analyzed the contrast between time-series and cross-section analysis. The fifth (joint with M.A. Girshick) on the demand for food provided a definitive treatment of estimation of demand and supply functions; it carried out the execution of a five-equation structural model of the U.S. economy. The sixth was an interesting policy model of the interrelationship between the agricultural and the rural sector of the economy, which was fitted to U.S. data and addressed to questions of policy.

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50

Mishchenko, T. M. "Methodology and Models of Combined Modeling of Electromagnetic Pro-cesses in Electric Traction Systems." Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, no. 2(92) (April 15, 2021): 40–49. http://dx.doi.org/10.15802/stp2021/237404.

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Abstract:

Purpose. The main purpose of the work is the development of identification models and a new method of modeling electromagnetic processes in electric traction systems with simultaneous consideration of all its subsystems, as well as several feeder zones of the electrified section. Methodology. To achieve this purpose, the methods of mathematical modelling, the basics of the theory of random processes and the methodology of their probabilistic-statistical processing, the methods for solving integral equations and analysis of electric traction circuits in electric traction systems are used. Findings. The requirements to be met by an adequate, stochastic identification model of electric traction devices are established. The solution of Fredholm’s integral correlation equation of the first kind is performed. The analytical expression of the identification dynamic model of the electric locomotive DE–1 is obtained and its adequacy is checked. The methodology of combined modeling of electromagnetic processes in devices and subsystems of electric traction systems is developed and presented tabularly. Originality. For the first time it is proposed to use the pulse transition function as identification models of traction substation and traction network with electric rolling stock in predictive modeling of electromagnetic and electric power processes in electric traction systems. A new method has been developed, a method of complex modeling of electromagnetic and electric power processes in the system of electric traction with simultaneous consideration of all its subsystems, as well as several inter-substation zones of the electrified section. For the first time, a method of partitioning the correlation functions for solving an integral correlation equation has been proposed, which allows defining a pulse transition function as an identification model of any subsystem of an electric traction system. Practical value. The developed identification models and the method of combined modeling make it possible to predict electromagnetic processes simultaneously in all feeder zones of the electrified section of the electric traction system. The obtained identification model of the electric locomotive DE–1 can be adapted with its subsequent use in modeling processes in the traction circuits of electric locomotives of other types. The method of factorization of correlation functions used in solving the Volterra integral correlation equation of the first kind (convolution type) can be adapted to the solution of other integral equations, which describe the processes in electric traction systems.

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