TY - JOUR

T1 - The sizes and powers of some stochastic dominance tests

T2 - A Monte Carlo study for correlated and heteroskedastic distributions

AU - Lean, Hooi Hooi

AU - Wong, Wing-Keung

AU - Zhang, Xibin

N1 - Funding Information:
We are grateful to Professor Robert Beauwens and anonymous referees for their substantive comments that have significantly improved this manuscript. The second author would like to thank Professors Robert B. Miller and Howard E. Thompson for their continual guidance and encouragement. The research is partially supported by the National University of Singapore and Monash University.

PY - 2008/10

Y1 - 2008/10

N2 - Testing for stochastic dominance among distributions is an important issue in the study of asset management, income inequality, and market efficiency. This paper conducts Monte Carlo simulations to examine the sizes and powers of several commonly used stochastic dominance tests when the underlying distributions are correlated or heteroskedastic. Our Monte Carlo study shows that the test developed by Davidson and Duclos [R. Davidson, J.Y. Duclos, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica 68 (6) (2000) 1435-1464] has better size and power performances than two alternative tests developed by Kaur et al. [A. Kaur, B.L.S.P. Rao, H. Singh, Testing for second order stochastic dominance of two distributions, Econ. Theory 10 (1994) 849-866] and Anderson [G. Anderson, Nonparametric tests of stochastic dominance in income distributions, Econometrica 64 (1996) 1183-1193]. In addition, we find that when the underlying distributions are heteroskedastic, both the size and power of the test developed by Davidson and Duclos [R. Davidson, J.Y. Duclos, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica 68 (6) (2000) 1435-1464] are superior to those of the two alternative tests.

AB - Testing for stochastic dominance among distributions is an important issue in the study of asset management, income inequality, and market efficiency. This paper conducts Monte Carlo simulations to examine the sizes and powers of several commonly used stochastic dominance tests when the underlying distributions are correlated or heteroskedastic. Our Monte Carlo study shows that the test developed by Davidson and Duclos [R. Davidson, J.Y. Duclos, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica 68 (6) (2000) 1435-1464] has better size and power performances than two alternative tests developed by Kaur et al. [A. Kaur, B.L.S.P. Rao, H. Singh, Testing for second order stochastic dominance of two distributions, Econ. Theory 10 (1994) 849-866] and Anderson [G. Anderson, Nonparametric tests of stochastic dominance in income distributions, Econometrica 64 (1996) 1183-1193]. In addition, we find that when the underlying distributions are heteroskedastic, both the size and power of the test developed by Davidson and Duclos [R. Davidson, J.Y. Duclos, Statistical inference for stochastic dominance and for the measurement of poverty and inequality, Econometrica 68 (6) (2000) 1435-1464] are superior to those of the two alternative tests.

KW - Correlated distributions

KW - Grid points

KW - Heteroskedasticity

KW - Stochastic dominance

UR - http://www.scopus.com/inward/record.url?scp=48549107525&partnerID=8YFLogxK

U2 - 10.1016/j.matcom.2007.09.002

DO - 10.1016/j.matcom.2007.09.002

M3 - Journal article

AN - SCOPUS:48549107525

SN - 0378-4754

VL - 79

SP - 30

EP - 48

JO - Mathematics and Computers in Simulation

JF - Mathematics and Computers in Simulation

IS - 1

ER -