000			02029nab a2200217 a 4500
999			_c199462 _d199462
003			OSt
005			20200226102031.0
008			191218s20162014xxu||||| |||| 00| 0 eng d
040			_aCO-BoUGC _cCO-BoUGC
100	1		_aShipman, Jonathan E. _9177811
245	1	0	_aPropensity score matching in accounting research _cJonathan E. Shipman, Quinn T. Swanquist & Robert L. Whited
300			_aPáginas 213 a la 244
520	3		_aPropensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008–2014. We document a significant increase in the use of PSM from zero studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composition and estimates of the ATE. We conclude with suggestions for future research considering the use of matching methods.
650	1	4	_991036 _aContabilidad _xInvestigaciones _vPublicaciones seriadas
690			_aPropensity Score Matching (PSM) _9177812
690			_aPuntaje de propensión _9177813
700	1		_aSwanquist, Quinn T. _9177814
700	1		_aWhited, Robert L. _9177815
773	0		_082265 _9377625 _aThe accounting review 2017 V.92 No.1 (Jan) _o0000002032281 _x0001-4826 (papel) _h32 páginas _nIncluye tablas, figuras, referencias bibliográficas y apéndices
942			_2ddc _cART