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Lad Asymptotics Under Conditional Heteroskedasticity With Possibly Infinite Error Densities

Author

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

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.

Suggested Citation

  • Cho, Jin Seo & Han, Chirok & Phillips, Peter C.B., 2010. "Lad Asymptotics Under Conditional Heteroskedasticity With Possibly Infinite Error Densities," Econometric Theory, Cambridge University Press, vol. 26(3), pages 953-962, June.
    • Handle: RePEc:cup:etheor:v:26:y:2010:i:03:p:953-962_99
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    Cited by:

    1. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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