TY - JOUR
T1 - An improved bootstrap test for restricted stochastic dominance
AU - Lok, Thomas M.
AU - Tabri, Rami V.
N1 - Funding Information:
This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the Melbourne Institute. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/10
Y1 - 2021/10
N2 - Bootstrap Testing for restricted stochastic dominance of a pre-specified order between two distributions is of interest in many areas of economics. This paper develops a new method for improving the performance of such tests that employ a moment selection procedure: tilting the empirical distribution in the moment selection procedure. We propose that the amount of tilting be chosen to maximize the empirical likelihood subject to the restrictions of the null hypothesis, which are a continuum of unconditional moment inequality conditions. We characterize sets of population distributions on which a modified test is (i) asymptotically equivalent to its non-modified version to first-order, and (ii) superior to its non-modified version according to local power when the sample size is large enough. We report simulation results that show the modified versions of leading tests are noticeably less conservative than their non-modified counterparts and have improved power. Finally, an empirical example is discussed to illustrate the proposed method.
AB - Bootstrap Testing for restricted stochastic dominance of a pre-specified order between two distributions is of interest in many areas of economics. This paper develops a new method for improving the performance of such tests that employ a moment selection procedure: tilting the empirical distribution in the moment selection procedure. We propose that the amount of tilting be chosen to maximize the empirical likelihood subject to the restrictions of the null hypothesis, which are a continuum of unconditional moment inequality conditions. We characterize sets of population distributions on which a modified test is (i) asymptotically equivalent to its non-modified version to first-order, and (ii) superior to its non-modified version according to local power when the sample size is large enough. We report simulation results that show the modified versions of leading tests are noticeably less conservative than their non-modified counterparts and have improved power. Finally, an empirical example is discussed to illustrate the proposed method.
KW - Bootstrap test
KW - Contact set
KW - Empirical likelihood
KW - Restricted stochastic dominance
KW - Semi-infinite program
UR - http://www.scopus.com/inward/record.url?scp=85099503586&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2019.08.016
DO - 10.1016/j.jeconom.2019.08.016
M3 - Article
AN - SCOPUS:85099503586
SN - 0304-4076
VL - 224
SP - 371
EP - 393
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 2
ER -