Goodness-of-fit test based on empirical characteristic function

This paper discusses a goodness-of-fit test that uses the integral of the squared modulus of the difference between the empirical characteristic function of the sample data and the characteristic function of the hypothesized distribution. Monte Carlo procedures are employed to obtain the empirical percentage points for testing the fit of normal, logistic and exponential distributions with unknown location and scale parameters. Results of Monte Carlo power comparisons with other well-developed goodness-of-fit tests are summarized. The proposed test is shown to have superior power for testing the fit of the logistic distribution (for moderate sample sizes) against a wide range of alternative distributions.