Calzolari, Giorgio and Di Iorio, Francesca and Fiorentini, Gabriele (1996): Control variates for variance reduction in indirect inference: interest rate models in continuous time.
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Abstract
Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for estimating stochastic differential equations. They adjust for the bias (inconsistency) caused by discretization of the underlying stochastic process, which is in continuous time. The price to be paid is an increased variance of the estimated parameters. There is, in fact, an additional component of the variance, which depends on the stochastic simulation involved in the estimation procedure. To reduce this udesirable effect one should enlarge the number of simulations (or the length of each simulation) and thus the computation cost. Alternatively, this paper shows how variance reduction can be achieved, at virtually no additional computation cost, by use of control variates. The OrnsteinUhlenbeck equation, used by Vasicek to model the short term interest rate in continuous time, and the so called square root equation, used by Cox, Ingersoll and Ross, are explicitly considered and experimented with. Monte Carlo experiments show that, for some parameters of interest, a global efficiency gain about 35%45% over the simplest indirect estimator is obtained at about the same computation cost.
Item Type:  MPRA Paper 

Original Title:  Control variates for variance reduction in indirect inference: interest rate models in continuous time 
Language:  English 
Keywords:  Monte Carlo; variance reduction techniques; control variates; indirect inference; discretization; shortterm interest rate; stochastic equation 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation 
Item ID:  23160 
Depositing User:  Giorgio Calzolari 
Date Deposited:  10. Jun 2010 04:26 
Last Modified:  14. Feb 2013 13:01 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/23160 
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