Regression quantiles for unstable autoregressive models

Shiqing Ling () and Michael McAleer

Journal of Multivariate Analysis, 2004, vol. 89, issue 2, 304-328

Abstract: This paper investigates regression quantiles (RQ) for unstable autoregressive models. The uniform Bahadur representation of the RQ process is obtained. The joint asymptotic distribution of the RQ process is derived in a unified manner for all types of characteristic roots on or outside the unit circle. It involves stochastic integrals in terms of a sequence of independent and identically distributed multivariate Brownian motions with correlated components. The related L-estimator is also discussed. The asymptotic distributions of the RQ and the L-estimator corresponding to the nonstationary componentwise arguments can be transformed into a function of a normal random variable and a sequence of i.i.d. univariate Brownian motions. This is different from the analysis based on the LSE in the literature. As an auxiliary theorem, a weak convergence of a randomly weighted residual empirical process to the stochastic integral of a Kiefer process is established. The results obtained in this paper provide an asymptotic theory for nonstationary time series processes, which can be used to construct robust unit root tests.

Keywords: Unit; root; Quantile; estimation; Unstable; AR; models; Randomly; weighted; residual; empirical; process (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Regression Quantiles for Unstable Autoregressive Models (2003)
Working Paper: Regression Quantiles for Unstable Autoregressive Models (2001)
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