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.
Bagnato, L., Punzo, A., Checking Serial Independence of Residuals from a Nonlinear Model, in Gaul, W., Geyer-Shulz, A., Schmidt-Thieme, L., Kunze, J. (ed.), Challenges at the Interface of Data Analysis, Computer Science and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlino 2012: 203- 211. 10.1007/978-3-642-24466-7_21 [http://hdl.handle.net/10807/40392]
Checking Serial Independence of Residuals from a Nonlinear Model
Bagnato, Luca;
2012
Abstract
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.
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