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 -
