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Maximum Likelihood Estimation of a Unit Root Bilinear Model with an Application to Prices

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  • Hristova Daniela

    () (City University London)

Abstract

We estimate a unit root bilinear process using the Maximum Likelihood method with log-likelihood function constructed by means of the Kalman filter, and evaluate the finite sample properties of this estimator.One hundred and five world-wide price series are tested for unit root bilinearity applying the test suggested by Charemza et al. (forthcoming). Applying the Maximum Likelihood estimator based on the Kalman filter, the null hypothesis of no bilinearity is rejected for 39 out of 105 series at the 5% level of significance. Most of the significant unit root bilinear coefficient estimates are explosive.

Suggested Citation

  • Hristova Daniela, 2005. "Maximum Likelihood Estimation of a Unit Root Bilinear Model with an Application to Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-15, March.
  • Handle: RePEc:bpj:sndecm:v:9:y:2005:i:1:n:4
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    References listed on IDEAS

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    1. Hinich, Melvin J & Patterson, Douglas M, 1985. "Evidence of Nonlinearity in Daily Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(1), pages 69-77, January.
    2. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    3. J. D. Byers & D. A. Peel, 1995. "Bilinear quadratic ARCH and volatility spillovers in inter-war exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 2(7), pages 215-219.
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    Cited by:

    1. Daiki Maki, 2013. "Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications," Empirical Economics, Springer, vol. 45(1), pages 605-625, August.

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