A nonparametric approach to test for predictability

• We propose a nonparametric method to test for predictability.
• Linear and nonlinear DGPs are considered in Monte Carlo analysis.
• Our test displays more corrected size than three existing methods.
• Our test has higher power than two nonparametric causality tests.

Predictability of macroeconomic and financial variables is an important issue in economics. In this paper, we propose a nonparametric test for the predictability of the direction of price changes. The Monte Carlo simulation results show that our method displays better finite-sample property than the traditional parametric Granger causality test (Granger, 1969) and two nonparametric causality tests of Hiemstra and Jones (1994) and Diks and Panchenko (2006).