Abstract:
In this paper, I investigate the power of the Granger and Lee model of asymmetry via bootstrap and
Monte Carlo techniques. The simulation results indicate that sample size, level of asymmetry and the amount
of noise in the data generating process are important determinants of the power of the test for asymmetry
based on bootstrap and Monte Carlo techniques. Additionally, the simulation results suggest that both
bootstrap and Monte Carlo methods are successful in rejecting the false null hypothesis of symmetric
adjustment in large samples with small error size and strong levels of asymmetry. In large samples, with
small error size and strong levels of asymmetry, the results suggest that asymmetry test based on Monte
Carlo methods achieve greater power gains when compared with the test for asymmetry based on bootstrap.
However, in small samples, with large error size and subtle levels of asymmetry, the results suggest that
asymmetry test based on bootstrap is more powerful than those based on the Monte Carlo methods. I
conclude that both bootstrap and Monte Carlo algorithms provide valuable tools for investigating the power
of the test of asymmetry
