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CONSISTENT AND ASYMPTOTICALLY NORMAL ESTIMATORS FOR PERIODIC BILINEAR MODELS

Bibi, Abdelouahab;Gautier, Antony

  • Received : 2008.04.15
  • Accepted : 2010.03.17
  • Published : 2010.09.30

Abstract

In this paper, a distribution free approach to the parameter estimation of a simple bilinear model with periodic coefficients is presented. The proposed method relies on minimum distance estimator based on the autocovariances of the squared process. Consistency and asymptotic normality of the estimator, as well as hypotheses testing, are derived. Numerical experiments on simulated data sets are presented to highlight the theoretical results.

Keywords

bilinear time series;periodic coefficients;minimum distance estimator;asymptotic normality;hypotheses testing

References

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Cited by

  1. On periodic time-varying bilinear processes: structure and asymptotic inference vol.25, pp.3, 2016, https://doi.org/10.1007/s10260-015-0344-5
  2. Minimum distance estimation of Markov-switching bilinear processes vol.50, pp.6, 2016, https://doi.org/10.1080/02331888.2016.1229783