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

Author

Listed:
  • D. Bond
  • M.J. Harrision
  • E.J. O, Brien

    (Department of Economics, Trinity College)

Abstract

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.

Suggested Citation

  • D. Bond & M.J. Harrision & E.J. O, Brien, 2005. "Investigating Nonlinearity: A Note on the Estimation of Hamilton's Random Field Regression Model," Trinity Economics Papers 200054, Trinity College Dublin, Department of Economics.
  • Handle: RePEc:tcd:tcduee:200054
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    File URL: http://www.tcd.ie/Economics/TEP/2005_papers/TEP4.pdf
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    References listed on IDEAS

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

    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2006. "Purchasing Power Parity: The Irish Experience Re-visited," Trinity Economics Papers tep200615, Trinity College Dublin, Department of Economics.
    5. Derek Bond & Michael J. Harrison & Niall Hession & Edward J. O'Brien, 2006. "Some Empirical Observations on the Forward Exchange Rate Anomaly," Trinity Economics Papers tep2006, Trinity College Dublin, Department of Economics.
    6. 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.

    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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