{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T21:51:21Z","timestamp":1788213081032,"version":"build-2803163510"},"reference-count":49,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators.<\/jats:p>","DOI":"10.1002\/jae.2903","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T05:16:38Z","timestamp":1652246198000},"page":"843-866","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Nowcasting tail risk to economic activity at a weekly frequency"],"prefix":"10.1002","volume":"37","author":[{"given":"Andrea","family":"Carriero","sequence":"first","affiliation":[{"name":"Queen Mary University of London  London UK"},{"name":"University of Bologna  Bologna Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd E.","family":"Clark","sequence":"additional","affiliation":[{"name":"Federal Reserve Bank of Cleveland  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimiliano","family":"Marcellino","sequence":"additional","affiliation":[{"name":"Bocconi University, CEPR, IGIER, BIDSA, and BAFFI  Milan Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2545"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2013.844155"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.20161923"},{"key":"e_1_2_9_5_1","article-title":"The term structure of growth\u2010at\u2010risk","author":"Adrian T.","year":"2020","journal-title":"American Economic Journal: Macroeconomics"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2013.767199"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.2307\/2951574"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1198\/jbes.2009.07205"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-53683-9.00004-9"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordhb\/9780195398649.013.0008"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssa.12092"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"Carriero A. Clark T. E. &Marcellino M.(2020a).Capturing macroeconomic tail risks with Bayesian vector autoregressions(Working Paper No. 20\u201302). Federal Reserve Bank of Cleveland.https:\/\/doi.org\/10.26509\/frbc-wp-202002","DOI":"10.26509\/frbc-wp-202002"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"Carriero A. Clark T. E. &Marcellino M.(2020b).Nowcasting tail risks to economic activity with many indicators(Working Paper No. 20\u201013R2). Federal Reserve Bank of Cleveland.https:\/\/doi.org\/10.26509\/frbc-wp-202013r2","DOI":"10.26509\/frbc-wp-202013r"},{"key":"e_1_2_9_14_1","article-title":"Addressing COVID\u201019 outliers in BVARs with stochastic volatility","author":"Carriero A.","year":"2021","journal-title":"Review of Economics and Statistics"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"Chavleishvili S. &Manganelli S.(2019).Forecasting and stress testing with quantile vector autoregression(Working Paper No. 2330). European Central Bank.","DOI":"10.2139\/ssrn.3489065"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.2307\/2527341"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1198\/jbes.2010.09248"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2379"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.1257"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2509"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.2307\/1392185"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1198\/jbes.2009.07214"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-53683-9.00001-3"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.06.010"},{"key":"e_1_2_9_25_1","doi-asserted-by":"crossref","unstructured":"Ferrara L. &Simoni A.(2019).When are Google data useful to nowcast GDP? An approach via pre\u2010selection and shrinkage(Working Paper No. 717). Banque de France.","DOI":"10.2139\/ssrn.3370917"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1439"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1198\/073500105000000018"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmoneco.2008.05.010"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2016.01.010"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1198\/jbes.2010.08110"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v090.i12"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2012.05.004"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-937X.00050"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2016.07.005"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1353\/mcb.2004.0056"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2873"},{"key":"e_1_2_9_38_1","first-page":"327","article-title":"Real\u2010time forecasting and scenario analysis using a large mixed frequency Bayesian VAR","volume":"18","author":"McCracken M. W.","year":"2021","journal-title":"International Journal of Central Banking"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1108\/S0731-90532021000043A004"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2018.10.008"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1353\/eca.2020.0002"},{"key":"e_1_2_9_42_1","doi-asserted-by":"crossref","unstructured":"Reichlin L. Ricco G. &Hasenzagl T.(2020).Financial variables as predictors of real growth vulnerability(Discussion Paper No. 05\/2020). Deutsche Bundesbank.","DOI":"10.2139\/ssrn.3556506"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2014.954707"},{"key":"e_1_2_9_44_1","doi-asserted-by":"crossref","unstructured":"Schorfheide F. &Song D.(2020).Real\u2010time forecasting with a (standard) mixed\u2010frequency VAR during a pandemic(Working Paper No. 29535).National Bureau of Economic Research.https:\/\/doi.org\/10.3386\/w29535","DOI":"10.21799\/frbp.wp.2020.26"},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.1162\/REST_a_00608"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2017.1281815"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.05.014"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/for.3980050102"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.2307\/2171956"},{"key":"e_1_2_9_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-7152(01)00124-9"}],"container-title":["Journal of Applied Econometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jae.2903","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jae.2903","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jae.2903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T11:09:34Z","timestamp":1692702574000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jae.2903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.1002\/jae.2903"],"URL":"https:\/\/doi.org\/10.1002\/jae.2903","archive":["Portico"],"relation":{},"ISSN":["0883-7252","1099-1255"],"issn-type":[{"value":"0883-7252","type":"print"},{"value":"1099-1255","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,9]]},"assertion":[{"value":"2020-08-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-28","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}