Maximum Likelihood Estimation of a Unit Root Bilinear Model with an Application to Prices
AbstractWe 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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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 47.
Date of creation: 11 Aug 2004
Date of revision:
unit root bilinear process; non-linear process; Kalman filter; Simulated Annealing; prices;
Find related papers by 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 &bull Diffusion Processes
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-08-16 (All new papers)
- NEP-ECM-2004-08-16 (Econometrics)
- NEP-ETS-2004-08-16 (Econometric Time Series)
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