LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities

LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities

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 asymptoti...

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Bibliographic Details
Main Authors: CHO, Jin Seo, HAN, Chirok, Peter C. B. PHILLIPS
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
Asymptotic leptokurtosis
Convex function
Infinite density
Least absolute deviations
Median
Weak convergence
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/1819
https://ink.library.smu.edu.sg/context/soe_research/article/2818/viewcontent/LAD_Asymptotics_sv.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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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