Nonparametric detection of correlated errors

Tae Yoon Kim

Biometrika, 2004, vol. 91, issue 2, 491-496

Abstract: In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first-order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information. Copyright Biometrika Trust 2004, Oxford University Press.

Date: 2004
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