Graphical modelling of multivariate time series
We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences. The models are derived from ordina...
Gespeichert in:
| Veröffentlicht in: | Probability theory and related fields 2012-06, Vol.153 (1-2), p.233-268 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 268 |
|---|---|
| container_issue | 1-2 |
| container_start_page | 233 |
| container_title | Probability theory and related fields |
| container_volume | 153 |
| creator | Eichler, Michael |
| description | We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs each component series is represented by a single vertex and directed edges indicate possible Granger-causal relationships between variables while undirected edges are used to map the contemporaneous dependence structure. We introduce various notions of Granger-causal Markov properties and discuss the relationships among them and to other Markov properties that can be applied in this context. Examples for graphical time series models include nonlinear autoregressive models and multivariate ARCH models. |
| doi_str_mv | 10.1007/s00440-011-0345-8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1031317354</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2673888691</sourcerecordid><originalsourceid>FETCH-LOGICAL-c420t-20448f96385bf6211fad7fae33c2754dafce0533f9f06785767974e45398d0fe3</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWKs_wN2AGzdj7817llK0CgU3ug5xJqkp86jJjOC_N6UuRNDV3Xzn3MNHyCXCDQKoRQLgHEpALIFxUeojMkPOaElB8mMyA1S61CDwlJyltAUAyjidkcUq2t1bqG1bdEPj2jb0m2LwRTe1Y_iwMdjRFWPoXJFcDC6dkxNv2-Quvu-cvNzfPS8fyvXT6nF5uy5rTmHMXznXvpJMi1cvKaK3jfLWMVZTJXhjfe1AMOYrD1JpoaSqFHdcsEo34B2bk-tD7y4O75NLo-lCqvM-27thSgaBIUPFBM_o1S90O0yxz-sMlSi5kiDEfxRmOYprUDpTeKDqOKQUnTe7GDobPzNk9qLNQbTJos1etNln6CGTMttvXPzZ_FfoC3jXfPM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1017748078</pqid></control><display><type>article</type><title>Graphical modelling of multivariate time series</title><source>EBSCOhost Business Source Complete</source><source>Springer Nature - Complete Springer Journals</source><creator>Eichler, Michael</creator><creatorcontrib>Eichler, Michael</creatorcontrib><description>We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs each component series is represented by a single vertex and directed edges indicate possible Granger-causal relationships between variables while undirected edges are used to map the contemporaneous dependence structure. We introduce various notions of Granger-causal Markov properties and discuss the relationships among them and to other Markov properties that can be applied in this context. Examples for graphical time series models include nonlinear autoregressive models and multivariate ARCH models.</description><identifier>ISSN: 0178-8051</identifier><identifier>EISSN: 1432-2064</identifier><identifier>DOI: 10.1007/s00440-011-0345-8</identifier><identifier>CODEN: PTRFEU</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Autoregressive models ; Causality ; Constraint modelling ; Dependence ; Dynamic tests ; Economics ; Finance ; Graph representations ; Graph theory ; Graphical representations ; Graphs ; Independence ; Insurance ; Management ; Markov analysis ; Markov processes ; Mathematical and Computational Biology ; Mathematical and Computational Physics ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Modelling ; Multivariate analysis ; Nonlinearity ; Operations Research/Decision Theory ; Probability ; Probability Theory and Stochastic Processes ; Quantitative Finance ; Statistics for Business ; Studies ; Theoretical ; Time series ; Variables</subject><ispartof>Probability theory and related fields, 2012-06, Vol.153 (1-2), p.233-268</ispartof><rights>The Author(s) 2011</rights><rights>Springer-Verlag 2012</rights><rights>The Author(s) 2011. This work is published under http://creativecommons.org/licenses/by-nc/2.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-20448f96385bf6211fad7fae33c2754dafce0533f9f06785767974e45398d0fe3</citedby><cites>FETCH-LOGICAL-c420t-20448f96385bf6211fad7fae33c2754dafce0533f9f06785767974e45398d0fe3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00440-011-0345-8$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00440-011-0345-8$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids></links><search><creatorcontrib>Eichler, Michael</creatorcontrib><title>Graphical modelling of multivariate time series</title><title>Probability theory and related fields</title><addtitle>Probab. Theory Relat. Fields</addtitle><description>We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs each component series is represented by a single vertex and directed edges indicate possible Granger-causal relationships between variables while undirected edges are used to map the contemporaneous dependence structure. We introduce various notions of Granger-causal Markov properties and discuss the relationships among them and to other Markov properties that can be applied in this context. Examples for graphical time series models include nonlinear autoregressive models and multivariate ARCH models.</description><subject>Autoregressive models</subject><subject>Causality</subject><subject>Constraint modelling</subject><subject>Dependence</subject><subject>Dynamic tests</subject><subject>Economics</subject><subject>Finance</subject><subject>Graph representations</subject><subject>Graph theory</subject><subject>Graphical representations</subject><subject>Graphs</subject><subject>Independence</subject><subject>Insurance</subject><subject>Management</subject><subject>Markov analysis</subject><subject>Markov processes</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical and Computational Physics</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Modelling</subject><subject>Multivariate analysis</subject><subject>Nonlinearity</subject><subject>Operations Research/Decision Theory</subject><subject>Probability</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Quantitative Finance</subject><subject>Statistics for Business</subject><subject>Studies</subject><subject>Theoretical</subject><subject>Time series</subject><subject>Variables</subject><issn>0178-8051</issn><issn>1432-2064</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kEtLAzEUhYMoWKs_wN2AGzdj7817llK0CgU3ug5xJqkp86jJjOC_N6UuRNDV3Xzn3MNHyCXCDQKoRQLgHEpALIFxUeojMkPOaElB8mMyA1S61CDwlJyltAUAyjidkcUq2t1bqG1bdEPj2jb0m2LwRTe1Y_iwMdjRFWPoXJFcDC6dkxNv2-Quvu-cvNzfPS8fyvXT6nF5uy5rTmHMXznXvpJMi1cvKaK3jfLWMVZTJXhjfe1AMOYrD1JpoaSqFHdcsEo34B2bk-tD7y4O75NLo-lCqvM-27thSgaBIUPFBM_o1S90O0yxz-sMlSi5kiDEfxRmOYprUDpTeKDqOKQUnTe7GDobPzNk9qLNQbTJos1etNln6CGTMttvXPzZ_FfoC3jXfPM</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Eichler, Michael</creator><general>Springer-Verlag</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20120601</creationdate><title>Graphical modelling of multivariate time series</title><author>Eichler, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-20448f96385bf6211fad7fae33c2754dafce0533f9f06785767974e45398d0fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Autoregressive models</topic><topic>Causality</topic><topic>Constraint modelling</topic><topic>Dependence</topic><topic>Dynamic tests</topic><topic>Economics</topic><topic>Finance</topic><topic>Graph representations</topic><topic>Graph theory</topic><topic>Graphical representations</topic><topic>Graphs</topic><topic>Independence</topic><topic>Insurance</topic><topic>Management</topic><topic>Markov analysis</topic><topic>Markov processes</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical and Computational Physics</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Modelling</topic><topic>Multivariate analysis</topic><topic>Nonlinearity</topic><topic>Operations Research/Decision Theory</topic><topic>Probability</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Quantitative Finance</topic><topic>Statistics for Business</topic><topic>Studies</topic><topic>Theoretical</topic><topic>Time series</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eichler, Michael</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Probability theory and related fields</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eichler, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Graphical modelling of multivariate time series</atitle><jtitle>Probability theory and related fields</jtitle><stitle>Probab. Theory Relat. Fields</stitle><date>2012-06-01</date><risdate>2012</risdate><volume>153</volume><issue>1-2</issue><spage>233</spage><epage>268</epage><pages>233-268</pages><issn>0178-8051</issn><eissn>1432-2064</eissn><coden>PTRFEU</coden><abstract>We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs each component series is represented by a single vertex and directed edges indicate possible Granger-causal relationships between variables while undirected edges are used to map the contemporaneous dependence structure. We introduce various notions of Granger-causal Markov properties and discuss the relationships among them and to other Markov properties that can be applied in this context. Examples for graphical time series models include nonlinear autoregressive models and multivariate ARCH models.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00440-011-0345-8</doi><tpages>36</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0178-8051 |
| ispartof | Probability theory and related fields, 2012-06, Vol.153 (1-2), p.233-268 |
| issn | 0178-8051 1432-2064 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1031317354 |
| source | EBSCOhost Business Source Complete; Springer Nature - Complete Springer Journals |
| subjects | Autoregressive models Causality Constraint modelling Dependence Dynamic tests Economics Finance Graph representations Graph theory Graphical representations Graphs Independence Insurance Management Markov analysis Markov processes Mathematical and Computational Biology Mathematical and Computational Physics Mathematical models Mathematics Mathematics and Statistics Modelling Multivariate analysis Nonlinearity Operations Research/Decision Theory Probability Probability Theory and Stochastic Processes Quantitative Finance Statistics for Business Studies Theoretical Time series Variables |
| title | Graphical modelling of multivariate time series |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T13%3A32%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Graphical%20modelling%20of%20multivariate%20time%20series&rft.jtitle=Probability%20theory%20and%20related%20fields&rft.au=Eichler,%20Michael&rft.date=2012-06-01&rft.volume=153&rft.issue=1-2&rft.spage=233&rft.epage=268&rft.pages=233-268&rft.issn=0178-8051&rft.eissn=1432-2064&rft.coden=PTRFEU&rft_id=info:doi/10.1007/s00440-011-0345-8&rft_dat=%3Cproquest_cross%3E2673888691%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1017748078&rft_id=info:pmid/&rfr_iscdi=true |