Approximating the probability distribution of functions of random variables: A new approach
AbstractWe introduce a new approximation method for the distribution of functions of random variables that are real-valued. The approximation involves moment matching and exploits properties of the class of normal inverse Gaussian distributions. In the paper we we examine the how well the different approximation methods can capture the tail behavior of a function of random variables relative each other. This is obtain done by simulate a number functions of random variables and then investigate the tail behavior for each method. Further we also focus on the regions of unimodality and positive definiteness of the different approximation methods. We show that the new method provides equal or better approximations than Gram-Charlier and Edgeworth expansio
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 503.
Date of creation: 11 Aug 2004
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Approximation of random variables;
Other versions of this item:
- Anders Eriksson & Lars Forsberg & Eric Ghysels, 2004. "Approximating the Probability Distribution of Functions of Random Variables: A New Approach," CIRANO Working Papers 2004s-21, CIRANO.
- C0 - Mathematical and Quantitative Methods - - General
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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- Mencía, Javier & Sentana, Enrique, 2005.
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