Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies

This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel m...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2022-02
Hauptverfasser: Lenon Minorics, Turkmen, Caner, Kernert, David, Bloebaum, Patrick, Callot, Laurent, Janzing, Dominik
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Causality
Fatalities
Panels
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Lenon Minorics
Turkmen, Caner
Kernert, David
Bloebaum, Patrick
Callot, Laurent
Janzing, Dominik
description This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel members are dependent. We compare our approach against the most widely used Granger causality algorithm on panel data and show that our approach yields lower FDR at the same power for large sample sizes and panels with cross-sectional dependencies. Finally, we examine COVID-19 data about confirmed cases and deaths measured in countries/regions worldwide and show that our approach is able to discover the true causal relation between confirmed cases and deaths while state-of-the-art approaches fail.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2632524765</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2632524765</sourcerecordid><originalsourceid>FETCH-proquest_journals_26325247653</originalsourceid><addsrcrecordid>eNqNjUsKwjAUAIMgWLR3CLgO1JemdV-_GxHsvoT6rCkhqXkp4u3twgO4ms0MM2MJSLkR2xxgwVKiPssyKEpQSibsXCNF4zp-DNp1GPjFO1HpkbQ18cON41ft0BJ_m_jkVfBE4oZtNN5py3c4oLujaw3Sis0f2hKmPy7Z-rCvq5MYgn-N06Xp_RimihooJCjIy0LJ_6wvbFs8Qw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2632524765</pqid></control><display><type>article</type><title>Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies</title><source>Freely Accessible Journals</source><creator>Lenon Minorics ; Turkmen, Caner ; Kernert, David ; Bloebaum, Patrick ; Callot, Laurent ; Janzing, Dominik</creator><creatorcontrib>Lenon Minorics ; Turkmen, Caner ; Kernert, David ; Bloebaum, Patrick ; Callot, Laurent ; Janzing, Dominik</creatorcontrib><description>This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel members are dependent. We compare our approach against the most widely used Granger causality algorithm on panel data and show that our approach yields lower FDR at the same power for large sample sizes and panels with cross-sectional dependencies. Finally, we examine COVID-19 data about confirmed cases and deaths measured in countries/regions worldwide and show that our approach is able to discover the true causal relation between confirmed cases and deaths while state-of-the-art approaches fail.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Causality ; Fatalities ; Panels</subject><ispartof>arXiv.org, 2022-02</ispartof><rights>2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>782,786</link.rule.ids></links><search><creatorcontrib>Lenon Minorics</creatorcontrib><creatorcontrib>Turkmen, Caner</creatorcontrib><creatorcontrib>Kernert, David</creatorcontrib><creatorcontrib>Bloebaum, Patrick</creatorcontrib><creatorcontrib>Callot, Laurent</creatorcontrib><creatorcontrib>Janzing, Dominik</creatorcontrib><title>Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies</title><title>arXiv.org</title><description>This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel members are dependent. We compare our approach against the most widely used Granger causality algorithm on panel data and show that our approach yields lower FDR at the same power for large sample sizes and panels with cross-sectional dependencies. Finally, we examine COVID-19 data about confirmed cases and deaths measured in countries/regions worldwide and show that our approach is able to discover the true causal relation between confirmed cases and deaths while state-of-the-art approaches fail.</description><subject>Algorithms</subject><subject>Causality</subject><subject>Fatalities</subject><subject>Panels</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjUsKwjAUAIMgWLR3CLgO1JemdV-_GxHsvoT6rCkhqXkp4u3twgO4ms0MM2MJSLkR2xxgwVKiPssyKEpQSibsXCNF4zp-DNp1GPjFO1HpkbQ18cON41ft0BJ_m_jkVfBE4oZtNN5py3c4oLujaw3Sis0f2hKmPy7Z-rCvq5MYgn-N06Xp_RimihooJCjIy0LJ_6wvbFs8Qw</recordid><startdate>20220223</startdate><enddate>20220223</enddate><creator>Lenon Minorics</creator><creator>Turkmen, Caner</creator><creator>Kernert, David</creator><creator>Bloebaum, Patrick</creator><creator>Callot, Laurent</creator><creator>Janzing, Dominik</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220223</creationdate><title>Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies</title><author>Lenon Minorics ; Turkmen, Caner ; Kernert, David ; Bloebaum, Patrick ; Callot, Laurent ; Janzing, Dominik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_26325247653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Causality</topic><topic>Fatalities</topic><topic>Panels</topic><toplevel>online_resources</toplevel><creatorcontrib>Lenon Minorics</creatorcontrib><creatorcontrib>Turkmen, Caner</creatorcontrib><creatorcontrib>Kernert, David</creatorcontrib><creatorcontrib>Bloebaum, Patrick</creatorcontrib><creatorcontrib>Callot, Laurent</creatorcontrib><creatorcontrib>Janzing, Dominik</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lenon Minorics</au><au>Turkmen, Caner</au><au>Kernert, David</au><au>Bloebaum, Patrick</au><au>Callot, Laurent</au><au>Janzing, Dominik</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies</atitle><jtitle>arXiv.org</jtitle><date>2022-02-23</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel members are dependent. We compare our approach against the most widely used Granger causality algorithm on panel data and show that our approach yields lower FDR at the same power for large sample sizes and panels with cross-sectional dependencies. Finally, we examine COVID-19 data about confirmed cases and deaths measured in countries/regions worldwide and show that our approach is able to discover the true causal relation between confirmed cases and deaths while state-of-the-art approaches fail.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2022-02
issn 2331-8422
language eng
recordid cdi_proquest_journals_2632524765
source Freely Accessible Journals
subjects Algorithms
Causality
Fatalities
Panels
title Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-11-30T22%3A03%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Testing%20Granger%20Non-Causality%20in%20Panels%20with%20Cross-Sectional%20Dependencies&rft.jtitle=arXiv.org&rft.au=Lenon%20Minorics&rft.date=2022-02-23&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2632524765%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2632524765&rft_id=info:pmid/&rfr_iscdi=true