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CONSISTENT ESTIMATION OF RESIDUAL VARIANCE IN REGULATORY EVENT STUDIES
Author(s) -
Ingram Marcus A.,
Ingram Virginia C.
Publication year - 1993
Publication title -
journal of financial research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.319
H-Index - 49
eISSN - 1475-6803
pISSN - 0270-2592
DOI - 10.1111/j.1475-6803.1993.tb00136.x
Subject(s) - ordinary least squares , statistics , econometrics , monte carlo method , mathematics , residual , correlation , event (particle physics) , variance (accounting) , statistical hypothesis testing , residual sum of squares , regression , economics , total least squares , algorithm , quantum mechanics , physics , geometry , accounting
This study presents new evidence on alternative methods used to test for abnormal returns in regulatory event studies where cross‐sectional correlation in residuals is significant. Results contradict earlier studies that find no advantages to using joint generalized least squares (JGLS) methods over ordinary least squares (OLS). We find that in an actual regulatory event study cross‐correlation is significant, and that failing to correct for this correlation results in substantially higher calculated F ‐statistics. In Monte Carlo simulations we find that OLS test statistics are not well specified when residuals exhibit cross‐sectional correlation at levels that are reasonable to expect in daily return data, while JGLS test statistics are well specified. The study includes tests of the effective power of the OLS and JGLS statistics.