Nonparametric Test for the Form of Parametric Regression with Time Series Errors
نویسندگان
چکیده
We propose a new nonparametric method for testing the parametric form of a regression function in the presence of time series errors. The test is motivated by recent advancement in the theory of ANOVA with large number of factor levels and also utilizes a new difference-based estimation method in nonparametric regression with time-series errors proposed by Hall and Van Keilegom (2003). The test statistic is asymptotically normal under the null and local alternative hypotheses. We also propose a bootstrap method to calculate the critical values and prove its consistency. In a Monte Carlo study, we demonstrate that this bootstrap procedure has good properties for moderate sample size.
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