The Strong Consistency of Quasi-Maximum Likelihood Estimators for p-order Random Coefficient Autoregressive (RCA) Models

Sankhya A ◽  
2021 ◽  
Author(s):  
Mohammed Benmoumen ◽  
Imane Salhi
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Random Coefficient ◽  
Quasi Maximum Likelihood ◽  
Random Coefficient Autoregressive

STRONG CONSISTENCY OF ESTIMATORS FOR MULTIVARIATE ARCH MODELS

1998 ◽  
Vol 14 (1) ◽  
pp. 70-86 ◽  
Author(s):  
Thierry Jeantheau
Keyword(s):  
Maximum Likelihood ◽  
Error Term ◽  
Strong Consistency ◽  
Asymptotic Properties ◽  
Maximum Likelihood Estimators ◽  
Multivariate Garch ◽  
Arch Models ◽  
Multivariate Garch Model ◽  
Quasi Maximum Likelihood ◽  
Derivatives Of

This paper deals with the asymptotic properties of quasi-maximum likelihood estimators for multivariate heteroskedastic models. For a general model, we give conditions under which strong consistency can be obtained; unlike in the current literature, the assumptions on the existence of moments of the error term are weak, and no study of the various derivatives of the likelihood is required. Then, for a particular model, the multivariate GARCH model with constant correlation, we describe the set of parameters where these conditions hold.

Strong Consistency of Maximum Likelihood Estimators n Exponential Sequential Model

2014 ◽  
Vol 519-520 ◽  
pp. 878-882
Author(s):  
Chang Ming Yin ◽  
Bo Hong Chen ◽  
Shuang Hua Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Regression Parameter ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Sequential Model ◽  
Mild Conditions ◽  
Strongly Consistent

For the exponential sequential model, we show that maximum likelihood estimator of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

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