Chin-Shan Chuang and Ngai Hang Chan
Empirical likelihood is developed for autoregressive models with
innovations that form a martingale difference sequence. Limiting
distributions of the log empirical likelihood ratio statistic for both
the stable and unstable cases are established. The local power of the
unit root test obtained via empirical likelihood is obtained for the
first-order model, and its finite-sample properties are assessed with
simulations. A resampling method is proposed to improve the
finite-sample performance of the empirical likelihood statistics. The
paper shows that empirical likelihood methodology compares favorably
with existing methods and demonstrates its potential for time series
with more general innovation structures.
Keywords: Empirical likelihood, dual likelihood, autoregressive models, unit root tests
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