PMC:7589389 / 43040-44000 JSONTXT

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T76","span":{"begin":60,"end":68},"obj":"Disease"},{"id":"T77","span":{"begin":208,"end":216},"obj":"Disease"},{"id":"T78","span":{"begin":557,"end":565},"obj":"Disease"},{"id":"T79","span":{"begin":740,"end":748},"obj":"Disease"}],"attributes":[{"id":"A76","pred":"mondo_id","subj":"T76","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A77","pred":"mondo_id","subj":"T77","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A78","pred":"mondo_id","subj":"T78","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A79","pred":"mondo_id","subj":"T79","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"6.4. The Impact of Volatility of Provincial Increase in New COVID-19 Cases\nAs a final robustness check, we examine whether our results are influenced by the volatility of provincial increase in new confirmed COVID-19 cases because high volatility of the number changing in the new confirmed cases may affect the extent of investors’ risk assessment. To tackle this concern, we substitute Conditioning_VAR in Model (3) with High_Volatility. Here High_Volatility is an indicator variable that equals one if the six-day standard deviation of the new confirmed COVID-19 cases is higher than or equal to the upper quartile value and zero otherwise. Table 13 shows the regression results on the moderate effect of volatility of the new confirmed COVID-19 cases. We find that the coefficients of CIPHT × High_Volatility are all statistically insignificant, suggesting that our results are not driven by the fluctuations in the number of new confirmed cases over time."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T196","span":{"begin":78,"end":79},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"6.4. The Impact of Volatility of Provincial Increase in New COVID-19 Cases\nAs a final robustness check, we examine whether our results are influenced by the volatility of provincial increase in new confirmed COVID-19 cases because high volatility of the number changing in the new confirmed cases may affect the extent of investors’ risk assessment. To tackle this concern, we substitute Conditioning_VAR in Model (3) with High_Volatility. Here High_Volatility is an indicator variable that equals one if the six-day standard deviation of the new confirmed COVID-19 cases is higher than or equal to the upper quartile value and zero otherwise. Table 13 shows the regression results on the moderate effect of volatility of the new confirmed COVID-19 cases. We find that the coefficients of CIPHT × High_Volatility are all statistically insignificant, suggesting that our results are not driven by the fluctuations in the number of new confirmed cases over time."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T35","span":{"begin":467,"end":476},"obj":"Chemical"}],"attributes":[{"id":"A35","pred":"chebi_id","subj":"T35","obj":"http://purl.obolibrary.org/obo/CHEBI_47867"}],"text":"6.4. The Impact of Volatility of Provincial Increase in New COVID-19 Cases\nAs a final robustness check, we examine whether our results are influenced by the volatility of provincial increase in new confirmed COVID-19 cases because high volatility of the number changing in the new confirmed cases may affect the extent of investors’ risk assessment. To tackle this concern, we substitute Conditioning_VAR in Model (3) with High_Volatility. Here High_Volatility is an indicator variable that equals one if the six-day standard deviation of the new confirmed COVID-19 cases is higher than or equal to the upper quartile value and zero otherwise. Table 13 shows the regression results on the moderate effect of volatility of the new confirmed COVID-19 cases. We find that the coefficients of CIPHT × High_Volatility are all statistically insignificant, suggesting that our results are not driven by the fluctuations in the number of new confirmed cases over time."}

{"project":"LitCovid-sentences","denotations":[{"id":"T304","span":{"begin":0,"end":4},"obj":"Sentence"},{"id":"T305","span":{"begin":5,"end":74},"obj":"Sentence"},{"id":"T306","span":{"begin":75,"end":349},"obj":"Sentence"},{"id":"T307","span":{"begin":350,"end":439},"obj":"Sentence"},{"id":"T308","span":{"begin":440,"end":643},"obj":"Sentence"},{"id":"T309","span":{"begin":644,"end":755},"obj":"Sentence"},{"id":"T310","span":{"begin":756,"end":960},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"6.4. The Impact of Volatility of Provincial Increase in New COVID-19 Cases\nAs a final robustness check, we examine whether our results are influenced by the volatility of provincial increase in new confirmed COVID-19 cases because high volatility of the number changing in the new confirmed cases may affect the extent of investors’ risk assessment. To tackle this concern, we substitute Conditioning_VAR in Model (3) with High_Volatility. Here High_Volatility is an indicator variable that equals one if the six-day standard deviation of the new confirmed COVID-19 cases is higher than or equal to the upper quartile value and zero otherwise. Table 13 shows the regression results on the moderate effect of volatility of the new confirmed COVID-19 cases. We find that the coefficients of CIPHT × High_Volatility are all statistically insignificant, suggesting that our results are not driven by the fluctuations in the number of new confirmed cases over time."}

{"project":"LitCovid-PubTator","denotations":[{"id":"265","span":{"begin":60,"end":68},"obj":"Disease"},{"id":"269","span":{"begin":208,"end":216},"obj":"Disease"},{"id":"270","span":{"begin":557,"end":565},"obj":"Disease"},{"id":"271","span":{"begin":740,"end":748},"obj":"Disease"}],"attributes":[{"id":"A265","pred":"tao:has_database_id","subj":"265","obj":"MESH:C000657245"},{"id":"A269","pred":"tao:has_database_id","subj":"269","obj":"MESH:C000657245"},{"id":"A270","pred":"tao:has_database_id","subj":"270","obj":"MESH:C000657245"},{"id":"A271","pred":"tao:has_database_id","subj":"271","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":"6.4. The Impact of Volatility of Provincial Increase in New COVID-19 Cases\nAs a final robustness check, we examine whether our results are influenced by the volatility of provincial increase in new confirmed COVID-19 cases because high volatility of the number changing in the new confirmed cases may affect the extent of investors’ risk assessment. To tackle this concern, we substitute Conditioning_VAR in Model (3) with High_Volatility. Here High_Volatility is an indicator variable that equals one if the six-day standard deviation of the new confirmed COVID-19 cases is higher than or equal to the upper quartile value and zero otherwise. Table 13 shows the regression results on the moderate effect of volatility of the new confirmed COVID-19 cases. We find that the coefficients of CIPHT × High_Volatility are all statistically insignificant, suggesting that our results are not driven by the fluctuations in the number of new confirmed cases over time."}