The asymptotic distribution of the maximum likelihood estimator for a vector time series model with long memory dependence

1997 ◽  
Vol 31 (4) ◽  
pp. 285-293 ◽  
Author(s):  
S. Sethuraman ◽  
I.V. Basawa
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Asymptotic Distribution ◽  
Long Memory ◽  
Time Series Model ◽  
Likelihood Estimator ◽  
Distribution Of The Maximum ◽  
Vector Time Series

Spurious Break

1995 ◽  
Vol 11 (4) ◽  
pp. 736-749 ◽  
Author(s):  
Luis C. Nunes ◽  
Chung-Ming Kuan ◽  
Paul Newbold
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Asymptotic Distribution ◽  
Likelihood Estimator ◽  
Data Generating Process ◽  
Quasi Maximum Likelihood ◽  
General Conditions

A quasi-maximum likelihood estimator of the break date is analyzed. Consistency of the estimator is demonstrated under very general conditions, provided that the data-generating process is not integrated. However, the asymptotic distribution of the estimator is quite different for time series that are integrated of order one. In that case, when there is no break, the analyst can be spuriously led to the estimation of a break near the middle of the time series.

scholarly journals Asymptotic Distribution of the Maximum Likelihood Estimator for a Stochastic Frontier Function Model with a Singular Information Matrix

1993 ◽  
Vol 9 (3) ◽  
pp. 413-430 ◽  
Author(s):  
Lung-Fei Lee
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Asymptotic Distribution ◽  
Information Matrix ◽  
Stochastic Frontier ◽  
Maximum Likelihood Estimates ◽  
Ratio Test ◽  
Likelihood Estimator ◽  
True Parameter ◽  
Distribution Of The Maximum

This paper investigates the asymptotic distribution of the maximum likelihood estimator in a stochastic frontier function when the firms are all technically efficient. For such a situation the true parameter vector is on the boundary of the parameter space, and the scores are linearly dependent. The asymptotic distribution of the maximum likelihood estimator is shown to be a mixture of certain truncated distributions. The maximum likelihood estimates for different parameters may have different rates of stochastic convergence. The model can be reparameterized into one with a regular likelihood function. The likelihood ratio test statistic has the usual mixture of chi-square distributions as in the regular case.

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