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In this paper we give an account of the approach to nonlinear econometric modelling proposed by Hamilton (2001) and briefly describe some of the methods of nonlinear optimization that may be used in the Gauss computer program provided by Hamilton for the implementation of his methodology. The performance of this program is investigated using data relating to Hamilton's example concerning the US Phillips curve, two versions of the Gauss software, and a range of alternative numerical optimization options and values for the Gauss parameter _oprteps. The impact of changes in initial parameter estimates and the use of pairs of optimization algorithms are also briefly examined. Finally, the effects of changes in the sample data on the results produced by Hamilton's procedure are explored. The results presented suggest some clear conclusions, which will be of value to those contemplating working with Hamilton's new method.

Investigating Nonlinearity: A Note on the Estimation of Hamilton's Random Field Regression Model