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An investigation of tests for linearity and the accuracy of likelihood based inference using random fields

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  • Christian M. Dahl

Abstract

We analyze the random field regression model approach recently suggested by Hamilton (2001, Econometrica, 69, 537--73). We show through extensive simulation studies that although the random field approach is indeed very closely related to the non-parametric spline smoother it seems to offer several advantages over the latter. First, tests for neglected nonlinearity based on Hamilton's random field approach seem to be more powerful than existing test statistics developed within the context of the multivariate spline smoother approach. Second, the convergence properties of the random field approach in limited samples appear to be significantly better than those of the multivariate spline smoother. Finally, when compared to the popular neural network approach the random field approach also performs very well. These results provide strong support for the view of Harvey and Koopman (2000, Econometrics Journal, 3, 84--107) that model-based kernels or splines have a sounder statistical justification than those typically used in non-parametric work. Copyright Royal Economic Society, 2002

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 5 (2002)
Issue (Month): 2 (06)
Pages: 263-284

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Handle: RePEc:ect:emjrnl:v:5:y:2002:i:2:p:263-284

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Cited by:
  1. Pablo Gonzalez & Mauricio Tejada, 2006. "No linealidades en la regla de política monetaria del Banco Central de Chile: una evidencia empírica," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 21(1), pages 81-115, July.
  2. 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.
  3. D H Kim, 2004. "Nonlinearity in the Term Structure," The School of Economics Discussion Paper Series 0401, Economics, The University of Manchester.
  4. 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.
  5. Dong Heon Kim, 2005. "Nonlinearity in the Term Structure," The School of Economics Discussion Paper Series 0528, Economics, The University of Manchester.
  6. Huang, Ho-Chuan, 2005. "Diverging evidence of convergence hypothesis," Journal of Macroeconomics, Elsevier, vol. 27(2), pages 233-255, June.
  7. 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.
  8. 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.
  9. Chihwa Kao & Yongmiao Hong, 2004. "Detecting Neglected Nonlinearity in Dynamic Panel Data with Time-Varying Conditional Heteroskedasticity," Econometric Society 2004 Far Eastern Meetings 753, Econometric Society.
  10. D H Kim & D R Osborn & M Sensier, 2002. "Nonlinearity in the Fed's Monetary Policy Rule," Centre for Growth and Business Cycle Research Discussion Paper Series 18, Economics, The Univeristy of Manchester.
  11. 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.
  12. D H Kim, 2005. "Nonlinearity in the Term Structure," Centre for Growth and Business Cycle Research Discussion Paper Series 51, Economics, The Univeristy of Manchester.
  13. Dahl, Christian M. & Hylleberg, Svend, 2004. "Flexible regression models and relative forecast performance," International Journal of Forecasting, Elsevier, vol. 20(2), pages 201-217.
  14. repec:wyi:journl:002062 is not listed on IDEAS

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