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Applications of a General Stable Law Regression Model

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  • Ian McHale
  • Patrick Laycock

Abstract

In this paper we present a method for performing regression with stable disturbances. The method of maximum likelihood is used to estimate both distribution and regression parameters. Our approach utilises a numerical integration procedure to calculate the stable density, followed by sequential quadratic programming optimisation procedures to obtain estimates and standard errors. A theoretical justification for the use of stable law regression is given followed by two real world practical examples of the method. First, we fit the stable law multiple regression model to housing price data and examine how the results differ from normal linear regression. Second, we calculate the beta coefficients for 26 companies from the Financial Times Ordinary Shares Index.

Suggested Citation

  • Ian McHale & Patrick Laycock, 2006. "Applications of a General Stable Law Regression Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1075-1084.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1075-1084
    DOI: 10.1080/02664760600746699
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    References listed on IDEAS

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    1. Aleksander Janicki & Aleksander Weron, 1994. "Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook9401, December.
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