Title

Time Series Regression with a Unit Root and Infinite Variance Errors

Document Type

Discussion Paper

Publication Date

4-1-1989

CFDP Number

897R

CFDP Revision Date

1989-08-01

CFDP Pages

29

Abstract

Chan and Tran give the limit theory for the least squares coefficient in a random walk with the iid errors that are in the domain of attraction of a stable law. This note discusses their results and provides generalizations to the case of I(q) processes with weakly dependent errors whose distributions are in the domain of attraction of a stable law. General unit root tests are also studied. It is shown that the semiparametric corrections suggested by the author for the finite variance case continue to work when the errors have infinite variance. The limit laws are expressed in terms of ratios of quadratic functionals of a stable process rather than Brownian motion. The correction terms that eliminate nuisance parameter dependencies are random in the limit and involve multiple stochastic integrals that may be written in terms of the quadratic variation of the limiting stable process.

This document is currently not available here.

Share

COinS