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Seeming regression of economic indices
dc.contributor.advisorLachout, Petr
dc.creatorKomzáková, Magdalena
dc.date.accessioned2017-03-27T12:02:57Z
dc.date.available2017-03-27T12:02:57Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/20.500.11956/4469
dc.description.abstractIn the time series analysis it often appears that two or more time series influence each other. When the generating stochastic processes of these series do not have stationary structure but they are stochastically non-stationary, i.e. the characteristic polynomial has a unit root, it happens that the regression modelling the dependence of some absolutely independent series gives statistically significant parameter estimations and statistics used to judge the model fitting do not indicate anything about its impropriety. This phenomenon is called seeming regression (spurious regression) and is solved with the theory of cointegration. We can say that when the series are cointegrated, their model shows their real dependence, not only the seeming one. Due to this fact, cointegration tests are also used for testing for the presence of seeming regression. These tests are based on unit root tests in generating process (or on stationarity tests), because time series can be cointegrated only if their linear combination is a stationary series.en_US
dc.languageČeštinacs_CZ
dc.language.isocs_CZ
dc.publisherUniverzita Karlova, Matematicko-fyzikální fakultacs_CZ
dc.titleZdánlivá regrese ekonomických ukazatelůcs_CZ
dc.typediplomová prácecs_CZ
dcterms.created2006
dcterms.dateAccepted2006-05-16
dc.description.departmentKatedra pravděpodobnosti a matematické statistikycs_CZ
dc.description.departmentDepartment of Probability and Mathematical Statisticsen_US
dc.description.facultyMatematicko-fyzikální fakultacs_CZ
dc.description.facultyFaculty of Mathematics and Physicsen_US
dc.identifier.repId41425
dc.title.translatedSeeming regression of economic indicesen_US
dc.contributor.refereeZvára, Karel
dc.identifier.aleph000869290
thesis.degree.nameMgr.
thesis.degree.levelmagisterskécs_CZ
thesis.degree.disciplineProbability, mathematical statistics and econometricsen_US
thesis.degree.disciplinePravděpodobnost, matematická statistika a ekonometriecs_CZ
thesis.degree.programMathematicsen_US
thesis.degree.programMatematikacs_CZ
uk.thesis.typediplomová prácecs_CZ
uk.taxonomy.organization-csMatematicko-fyzikální fakulta::Katedra pravděpodobnosti a matematické statistikycs_CZ
uk.taxonomy.organization-enFaculty of Mathematics and Physics::Department of Probability and Mathematical Statisticsen_US
uk.faculty-name.csMatematicko-fyzikální fakultacs_CZ
uk.faculty-name.enFaculty of Mathematics and Physicsen_US
uk.faculty-abbr.csMFFcs_CZ
uk.degree-discipline.csPravděpodobnost, matematická statistika a ekonometriecs_CZ
uk.degree-discipline.enProbability, mathematical statistics and econometricsen_US
uk.degree-program.csMatematikacs_CZ
uk.degree-program.enMathematicsen_US
thesis.grade.csVelmi dobřecs_CZ
thesis.grade.enVery gooden_US
uk.abstract.enIn the time series analysis it often appears that two or more time series influence each other. When the generating stochastic processes of these series do not have stationary structure but they are stochastically non-stationary, i.e. the characteristic polynomial has a unit root, it happens that the regression modelling the dependence of some absolutely independent series gives statistically significant parameter estimations and statistics used to judge the model fitting do not indicate anything about its impropriety. This phenomenon is called seeming regression (spurious regression) and is solved with the theory of cointegration. We can say that when the series are cointegrated, their model shows their real dependence, not only the seeming one. Due to this fact, cointegration tests are also used for testing for the presence of seeming regression. These tests are based on unit root tests in generating process (or on stationarity tests), because time series can be cointegrated only if their linear combination is a stationary series.en_US
uk.publication.placePrahacs_CZ
uk.grantorUniverzita Karlova, Matematicko-fyzikální fakulta, Katedra pravděpodobnosti a matematické statistikycs_CZ


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