Title

LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities

Document Type

Discussion Paper

Publication Date

6-1-2009

CFDP Number

1703

CFDP Pages

10

Abstract

Least absolute deviations (LAD) estimation of linear time-series models is considered under conditional heteroskedasticity and serial correlation. The limit theory of the LAD estimator is obtained without assuming the finite density condition for the errors that is required in standard LAD asymptotics. The results are particularly useful in application of LAD estimation to financial time series data.

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