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

Daniela Hristova

No 47, Computing in Economics and Finance 2004 from Society for Computational Economics

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 six world-wide price series are tested for unit root bilinearity applying the test suggested by Charemza et al. (2002b). Applying the Maximum Likelihood estimator based on the Kalman filter, the null hypothesis of no bilinearity is rejected for 40 out of 106 series at the 5% level of significance. Most of the significant unit root bilinear coefficient estimates are explosive

Keywords: unit root bilinear process, non-linear process, Kalman filter, Simulated Annealing, prices (search for similar items in EconPapers)
JEL-codes: C13 C22 E31 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-ecm and nep-ets
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