Abstract
In this article we discuss serial correlation in a linear time series regression context and serial dependence in a nonlinear time series context. We first discuss various tests for serial correlation for both estimated regression residuals and observed raw data. Particular attention is paid to the impact of parameter estimation uncertainty and conditional heteroskedasticity on the asymptotic distribution of test statistics. We discuss the drawback of serial correlation in nonlinear time series models and introduce a number of measures that can capture nonlinear serial dependence and reveal useful information about serial dependence.
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