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Proposes a strategy for modeling nonlinear relationships in time-series data, suggesting effective evaluation techniques.
Building models of nonlinear relationships are inherently more difficult than linear ones There are more possibilities, many more parameters and thus more mistakes can be made. It is suggested that a strategy be applied when attempting such modelling involving testing for linearity, considering just a few model types of parsimonious form and then performing post‐sample evaluation of the resulting models compared to a linear one. The strategy proposed is a ‘simple‐to‐general’ one and the application of a heteroskedasticity correction is not recommended
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Clive W. J. Granger (1993) studied this question.
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