Template-type: ReDIF-Paper 1.0
Author-Name: Siem Jan Koopman
Author-Email: s.j.koopman@feweb.vu.nl
Author-Workplace-Name:Free University Amsterdam, Amsterdam
Author-Name: Neil Shephard
Author-Email:neil.shephard@nuffield.ox.ac.uk 
Author-Homepage: http://www.nuff.ox.ac.uk/users/shephard/
Author-Workplace-Name: Nuffield College, Oxford University, Oxford
Author-Workplace-Homepage: http://www.nuff.ox.ac.uk/nuffield.html
Title: Unemployment, Labour Market Institutions and Shocks
Abstract:
 Importance sampling is used in many aspects of modern econometrics to
 approximate unsolvable integrals. Its reliable use requires the sampler to
 possess a variance, for this guarantees a square root speed of convergence
 and asymptotic normality of the estimator of the integral. However, this
 assumption is seldom checked. In this paper we propose to use extreme
 value theory to empirically assess the appropriateness of this assumption.
 We illustrate this method in the context of a maximum simulated likelihood
 analysis of the stochastic volatility model.
Keywords: Extreme value theory; Importance sampling; Simulation; Stochastic Volatility.
Length:14 pages
Creation-Date: 2002-06-01
Number:2002-W17
File-URL:http://www.nuff.ox.ac.uk/economics/papers/2002/w17/isdiagwp.pdf
File-Format: application/pdf
Handle: RePEc:nuf:econwp:0217