PMC:7589389 / 23998-24720 JSONTXT

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T60","span":{"begin":571,"end":579},"obj":"Disease"}],"attributes":[{"id":"A60","pred":"mondo_id","subj":"T60","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"We describe the regression model for the main test of H1. The regression models for cross-sectional tests are described in Section 5. To test H1, we apply the multiple regression model as follows:CAR = β0 + β1CIPHT + β2PRO_CASE + β3SIZE + β4ROA + β5CURR + β6R\u0026D + β7LOSS+ β8LEV + β9OPCF + β10TURN + β11CEO_AGE+ β12CEO_COM + β13CEO_TEN+ β14CEO_DUA + Week FE + Industry FE + Province FE + ε(2) where CAR refers to our two types of accumulative abnormal return (CAR [−1, 1] and CAR [−2, 2]), CIPHT is an indicator variable that equals one if there have been provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. Based on H1, we suppose a negative coefficient of CIPHT."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T92","span":{"begin":46,"end":50},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T93","span":{"begin":100,"end":105},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T94","span":{"begin":137,"end":141},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T95","span":{"begin":258,"end":261},"obj":"http://purl.obolibrary.org/obo/CLO_0008693"},{"id":"T96","span":{"begin":258,"end":261},"obj":"http://purl.obolibrary.org/obo/CLO_0008770"},{"id":"T97","span":{"begin":399,"end":402},"obj":"http://purl.obolibrary.org/obo/CLO_0002199"},{"id":"T98","span":{"begin":460,"end":467},"obj":"http://purl.obolibrary.org/obo/CLO_0009955"},{"id":"T99","span":{"begin":476,"end":479},"obj":"http://purl.obolibrary.org/obo/CLO_0002199"},{"id":"T100","span":{"begin":482,"end":486},"obj":"http://purl.obolibrary.org/obo/CLO_0050507"},{"id":"T101","span":{"begin":690,"end":691},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"We describe the regression model for the main test of H1. The regression models for cross-sectional tests are described in Section 5. To test H1, we apply the multiple regression model as follows:CAR = β0 + β1CIPHT + β2PRO_CASE + β3SIZE + β4ROA + β5CURR + β6R\u0026D + β7LOSS+ β8LEV + β9OPCF + β10TURN + β11CEO_AGE+ β12CEO_COM + β13CEO_TEN+ β14CEO_DUA + Week FE + Industry FE + Province FE + ε(2) where CAR refers to our two types of accumulative abnormal return (CAR [−1, 1] and CAR [−2, 2]), CIPHT is an indicator variable that equals one if there have been provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. Based on H1, we suppose a negative coefficient of CIPHT."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T8","span":{"begin":354,"end":356},"obj":"Chemical"},{"id":"T9","span":{"begin":368,"end":370},"obj":"Chemical"},{"id":"T10","span":{"begin":382,"end":384},"obj":"Chemical"},{"id":"T11","span":{"begin":502,"end":511},"obj":"Chemical"}],"attributes":[{"id":"A8","pred":"chebi_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/CHEBI_74712"},{"id":"A9","pred":"chebi_id","subj":"T9","obj":"http://purl.obolibrary.org/obo/CHEBI_74712"},{"id":"A10","pred":"chebi_id","subj":"T10","obj":"http://purl.obolibrary.org/obo/CHEBI_74712"},{"id":"A11","pred":"chebi_id","subj":"T11","obj":"http://purl.obolibrary.org/obo/CHEBI_47867"}],"text":"We describe the regression model for the main test of H1. The regression models for cross-sectional tests are described in Section 5. To test H1, we apply the multiple regression model as follows:CAR = β0 + β1CIPHT + β2PRO_CASE + β3SIZE + β4ROA + β5CURR + β6R\u0026D + β7LOSS+ β8LEV + β9OPCF + β10TURN + β11CEO_AGE+ β12CEO_COM + β13CEO_TEN+ β14CEO_DUA + Week FE + Industry FE + Province FE + ε(2) where CAR refers to our two types of accumulative abnormal return (CAR [−1, 1] and CAR [−2, 2]), CIPHT is an indicator variable that equals one if there have been provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. Based on H1, we suppose a negative coefficient of CIPHT."}

{"project":"LitCovid-sentences","denotations":[{"id":"T177","span":{"begin":0,"end":57},"obj":"Sentence"},{"id":"T178","span":{"begin":58,"end":133},"obj":"Sentence"},{"id":"T179","span":{"begin":134,"end":665},"obj":"Sentence"},{"id":"T180","span":{"begin":666,"end":722},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"We describe the regression model for the main test of H1. The regression models for cross-sectional tests are described in Section 5. To test H1, we apply the multiple regression model as follows:CAR = β0 + β1CIPHT + β2PRO_CASE + β3SIZE + β4ROA + β5CURR + β6R\u0026D + β7LOSS+ β8LEV + β9OPCF + β10TURN + β11CEO_AGE+ β12CEO_COM + β13CEO_TEN+ β14CEO_DUA + Week FE + Industry FE + Province FE + ε(2) where CAR refers to our two types of accumulative abnormal return (CAR [−1, 1] and CAR [−2, 2]), CIPHT is an indicator variable that equals one if there have been provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. Based on H1, we suppose a negative coefficient of CIPHT."}

{"project":"LitCovid-PubTator","denotations":[{"id":"165","span":{"begin":571,"end":579},"obj":"Disease"}],"attributes":[{"id":"A165","pred":"tao:has_database_id","subj":"165","obj":"MESH:C000657245"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"We describe the regression model for the main test of H1. The regression models for cross-sectional tests are described in Section 5. To test H1, we apply the multiple regression model as follows:CAR = β0 + β1CIPHT + β2PRO_CASE + β3SIZE + β4ROA + β5CURR + β6R\u0026D + β7LOSS+ β8LEV + β9OPCF + β10TURN + β11CEO_AGE+ β12CEO_COM + β13CEO_TEN+ β14CEO_DUA + Week FE + Industry FE + Province FE + ε(2) where CAR refers to our two types of accumulative abnormal return (CAR [−1, 1] and CAR [−2, 2]), CIPHT is an indicator variable that equals one if there have been provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. Based on H1, we suppose a negative coefficient of CIPHT."}