Nonstandard Errors

• Autoři: MENKVELD Albert J; DREBER Anna; HOLZMEISTER Felix; HUBER Juergen; JOHANNESSON Magnus; KIRCHLER Michael; NEUSUSS Sebastian; RAZEN Michael; WEITZEL Utz; ABAD-DIAZ David; ABUDY Menachem; ADRIAN Tobias; AIT-SAHALIA Yacine; AKMANSOY Olivier; ALCOCK Jamie T; ALEXEEV Vitali; ALOOSH Arash; AMATO Livia; AMAYA Diego; ANGEL James J; AVETIKIAN Alejandro T; BACH Amadeus; BAIDOO Edwin; BAKALLI Gaetan; BAO Li; BARBON Andrea; BASHCHENKO Oksana; BINDRA Parampreet C; BJONNES Geir H; BLACK Jeffrey R; BLACK Bernard S; BOGOEV Dimitar; SANTIAGO Bohorquez Correa; BONDARENKO Oleg; BOS Charles S; BOSCH-ROSA Ciril; BOURI Elie; BROWNLEES Christian; CALAMIA Anna; CAO Viet Nga; CAPELLE-BLANCARD Gunther; ROMERO Laura M Capera; CAPORIN Massimiliano; CARRION Allen; CASKURLU Tolga; CHAKRABARTY Bidisha; CHEN Jian; CHERNOV Mikhail; CHEUNG William; CHINCARINI Ludwig B; CHORDIA Tarun; CHOW Sheung-Chi; CLAPHAM Benjamin; COLLIARD Jean-Edouard; COMERTON-FORDE Carole; CURRAN Edward; DAO Thong; DARE Wale; DAVIES Ryan J; 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• Rok publikování: 2024
• Druh: Článek v odborném periodiku
• Časopis / Zdroj: JOURNAL OF FINANCE
• Fakulta / Pracoviště MU: Ekonomicko-správní fakulta
• Doi: http://dx.doi.org/10.1111/jofi.13337
• Popis: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.