scholarly journals On Bahadur asymptotic efficiency of the maximum likelihood and quasi-maximum likelihood estimators in Gaussian stationary processes

2000 ◽  
Vol 85 (1) ◽  
pp. 29-44 ◽  
Author(s):  
Yoshihide Kakizawa
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Efficiency ◽  
Maximum Likelihood Estimators ◽  
Stationary Processes ◽  
Quasi Maximum Likelihood

Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Antonis Demos ◽  
Dimitra Kyriakopoulou
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Bias Correction ◽  
Maximum Likelihood Estimators ◽  
Finite Sample ◽  
Egarch Model ◽  
Quasi Maximum Likelihood ◽  
Measure Of Performance ◽  
Analytical Expressions ◽  
Better Than

AbstractWe derive the analytical expressions of bias approximations for maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of the EGARCH (1,1) parameters that enable us to correct after the bias of all estimators. The bias-correction mechanism is constructed under the specification of two methods that are analytically described. We also evaluate the residual bootstrapped estimator as a measure of performance. Monte Carlo simulations indicate that, for given sets of parameters values, the bias corrections work satisfactory for all parameters. The proposed full-step estimator performs better than the classical one and is also faster than the bootstrap. The results can be also used to formulate the approximate Edgeworth distribution of the estimators.

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