In this paper the serial independence tests known as SIS (Serial Independence Simultaneous) and SICS (Serial Independence Chi-Square) are considered. These tests are here contextualized in the model validation phase for nonlinear models in which the underlying assumption of serial independence is tested on the estimated residuals. Simulations are used to explore the performance of the tests, in terms of size and power, once a linear/nonlinear model is fitted on the raw data. Results underline that both the tests are powerful against various types of alternatives.
|Title of host publication||Challenges at the Interface of Data Analysis, Computer Science and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization|
|Editors||Wolfgang Gaul, Andreas Geyer-Shulz, Lars Schmidt-Thieme, Jonas Kunze|
|Number of pages||9|
|Publication status||Published - 2012|
- Model validation
- Nonlinear time series