Nonparametric test for the form of parametric regression with time series errors
(2007) Statistica Sinica — Vol. 17, n° 1, p. 369-386 (2007)
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- Abstract
- 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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Wang, L., & Van Keilegom, I. (2007). Nonparametric test for the form of parametric regression with time series errors. Statistica Sinica, 17(1), 369-386. https://hdl.handle.net/2078.5/82168 (Original work published 2007)