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

Author

Listed:
  • Bond Derek

    () (University of Ulster)

  • Harrison Michael J.

    () (Trinity College Dublin)

  • O'Brien Edward J.

    () (Trinity College Dublin)

Abstract

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.

Suggested Citation

  • Bond Derek & Harrison Michael J. & O'Brien Edward J., 2005. "Investigating Nonlinearity: A Note on the Estimation of Hamilton's Random Field Regression Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(3), pages 1-43, September.
  • Handle: RePEc:bpj:sndecm:v:9:y:2005:i:3:n:2
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    References listed on IDEAS

    as
    1. Hamilton, James D, 2001. "A Parametric Approach to Flexible Nonlinear Inference," Econometrica, Econometric Society, vol. 69(3), pages 537-573, May.
    2. 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.
    3. 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, Department of Economics and Business Economics, Aarhus University.
    4. 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, pages 141-164.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2007. "Demand for Money: A Study in Testing Time Series for Long Memory and Nonlinearity," The Economic and Social Review, Economic and Social Studies, vol. 38(1), pages 1-24.
    2. Derek Bond & Michael Harrison & Niall Hession & Edward O'Brien, 2010. "Nonlinearity as an explanation of the forward exchange rate anomaly," Applied Economics Letters, Taylor & Francis Journals, vol. 17(13), pages 1237-1239.
    3. Whelan, Karl, 2008. "Consumption and expected asset returns without assumptions about unobservables," Journal of Monetary Economics, Elsevier, pages 1209-1221.
    4. Peter Winker & Dietmar Maringer, 2009. "The convergence of estimators based on heuristics: theory and application to a GARCH model," Computational Statistics, Springer, vol. 24(3), pages 533-550, August.
    5. Bond, Derek & Harrison, Michael J & O’Brien, Edward J., 2006. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Research Technical Papers 2/RT/06, Central Bank of Ireland.
    6. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    7. Liam Delaney & Carol Newman & Brian Nolan, 2006. "Reference dependent financial satisfaction over the course of the Celtic Tiger : a panel analysis utilising the Living in Ireland Survey 1994-2001," Open Access publications 10197/588, School of Economics, University College Dublin.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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