LAD asymptotics under conditional heteroskedasticity with possibly infinite error densities

Jin Seo Cho; Chirok Han; Peter C.B. Phillips

doi:10.1017/S0266466609990703

LAD asymptotics under conditional heteroskedasticity with possibly infinite error densities

Jin Seo Cho, Chirok Han, Peter C.B. Phillips

Department of Economics

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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.

Original language	English
Pages (from-to)	953-962
Number of pages	10
Journal	Econometric Theory
Volume	26
Issue number	3
DOIs	https://doi.org/10.1017/S0266466609990703
Publication status	Published - 2010 Jun

ASJC Scopus subject areas

Social Sciences (miscellaneous)
Economics and Econometrics

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title = "LAD asymptotics under conditional heteroskedasticity with possibly infinite error densities",

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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AU - Cho, Jin Seo

AU - Han, Chirok

AU - Phillips, Peter C.B.

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N2 - 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.

AB - 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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M3 - Article

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JO - Econometric Theory

JF - Econometric Theory

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ER -

LAD asymptotics under conditional heteroskedasticity with possibly infinite error densities

Abstract

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