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

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.

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Year of publication:	2007
Authors:	Hristova, Daniela
Published in:	Studies in Nonlinear Dynamics & Econometrics. - Berkeley Electronic Press. - Vol. 9.2007, 1, p. 1199-1199
Publisher:	Berkeley Electronic Press
Subject:	unit root bilinear process \| non-linear process \| Kalman filter \| simulated annealing \| prices

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Extent:	application/pdf
Type of publication:	Article
Notes:	oai:bepress:
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10004966256