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LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities

Author

Listed:
  • Peter C.B.Phillips

    (Yale University, University of Auckland,University of York & Singapore Management University)

  • Jin Seo Cho

    (Korea University)

  • Chirok Han

    (Korea University)

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

  • Peter C.B.Phillips & Jin Seo Cho & Chirok Han, 2009. "LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities," Working Papers CoFie-02-2009, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    • Handle: RePEc:skb:wpaper:cofie-02-2009
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    File URL: http://www.smu.edu.sg/institutes/skbife/downloads/CoFiE/Working%20Papers/LAD%20Asymptotics%20under%20Conditional%20Heteroskedasticity%20with%20Possibly%20Infinite%20Error%20Densities.pdf
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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.

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    Keywords

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    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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