Investigating Nonlinearity: A Note on the Estimation of Hamilton’s Random Field Regression Model
This is a revised and extended version of the authors’ 2003 Trinity Economic Paper. It describes Hamilton’s (2001) approach to nonlinear econometric modelling and some of the methods of nonlinear optimization, as before, but adds significantly to the investigation of Hamilton’s Gauss program for the implementation of his methodology. Specifically, it reports on the performance of this program using data relating to Hamilton’s US Phillips curve example, the use of two versions of the Gauss software and a range of numerical optimization options. It also examines the impact of changes in initial parameter estimates, the use of algorithm switching strategies, and the e?ects of changes in the sample data on the results produced by Hamilton’s procedure. The new results presented suggest some further clear conclusions that will be of value to those using Hamilton’s method.
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- Hamilton, James D., 1999.
"A Parametric Approach to Flexible Nonlinear Inference,"
University of California at San Diego, Economics Working Paper Series
qt68s8157x, Department of Economics, UC San Diego.
- Hamilton, James D, 2001. "A Parametric Approach to Flexible Nonlinear Inference," Econometrica, Econometric Society, vol. 69(3), pages 537-73, May.
- Christian M. Dahl & Yu Qin, 2008. "The limiting behavior of the estimated parameters in a misspecified random field regression model," CREATES Research Papers 2008-45, School of Economics and Management, University of Aarhus.
- Dahl, Christian M. & Gonzalez-Rivera, Gloria, 2003. "Testing for neglected nonlinearity in regression models based on the theory of random fields," Journal of Econometrics, Elsevier, vol. 114(1), pages 141-164, May.
- D. Bond & M. Harrison & E.J. O'Brien, 2003. "Investigating Nonlinearity: A Note on the Implementation of Hamilton's Methodology," Trinity Economics Papers 200312, Trinity College Dublin, Department of Economics.
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