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

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  • Daniela Hristova

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

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  • Daniela Hristova, 2004. "Maximum Likelihood Estimation of a Unit Root Bilinear Model with an Application to Prices," Computing in Economics and Finance 2004 47, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:47
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    References listed on IDEAS

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    More about this item

    Keywords

    unit root bilinear process; non-linear process; Kalman filter; Simulated Annealing; prices;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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