Add to ORKGNumerical comparisons are made of the distribution of the estimator of the parameter in a first-order autoregressive process. Results of Reeves (1972) are compared with those of Arato & Bencziir (1970, 1972) which use a model in continuous time. Some key words: Autoregressive process; Maximum likelihood estimation; Simulation; Time series estimation. Suppose that Yt is a first-order autoregressive process of zero mean, so that for i = 1,2,... moreover where {.EJare independently and identically distributed in */V(0,1) and Yo has the stationary distribution N{0,1/(1 —A2)}. Reeves (1972) has analyzed the distribution o
Assuming that the errors of an autoregressive process form a sequence of martingale differences, the...
2 We investigate autoregressive approximations of multiple frequency I(1), MFI(1), pro-cesses. The u...
The behaviour of the sample autocorrelation coefficients is important for the identification of the ...
A discussion is given of some time series models driven by iid noise having a discrete component. In...
This paper derives the exact distribution of the maximum likelihood estimator of a first-order linea...
summary:Discrete autoregressive process of the first order is considered. The process is observed at...
This paper investigates the finite sample distribution of the least squares estimator of the autoreg...
By way of Monte Carlo techniques, this paper compares the moderate sample size properties of three m...
A new class of time series models known as Generalized Autoregressive of order one with first-order ...
SUMMARY. For the stationary first order autoregressive models with irregularly ob-served or missing ...
Binomial AR(1) process is a model for integer-valued time series with a fi- nite range and discrete ...
The likelihoood function of the Gaussian MA(1) zero-mean can be expressed in terms of the variance o...
The class of autoregressive (AR) processes is extensively used to model temporal dependence in obser...
The likelihood function for an autoregressive-moving average process is obtained using Kalman filter...
It was recently proved that any strictly stationary stochastic process can be viewed as an autoregre...
Assuming that the errors of an autoregressive process form a sequence of martingale differences, the...
2 We investigate autoregressive approximations of multiple frequency I(1), MFI(1), pro-cesses. The u...
The behaviour of the sample autocorrelation coefficients is important for the identification of the ...
A discussion is given of some time series models driven by iid noise having a discrete component. In...
This paper derives the exact distribution of the maximum likelihood estimator of a first-order linea...
summary:Discrete autoregressive process of the first order is considered. The process is observed at...
This paper investigates the finite sample distribution of the least squares estimator of the autoreg...
By way of Monte Carlo techniques, this paper compares the moderate sample size properties of three m...
A new class of time series models known as Generalized Autoregressive of order one with first-order ...
SUMMARY. For the stationary first order autoregressive models with irregularly ob-served or missing ...
Binomial AR(1) process is a model for integer-valued time series with a fi- nite range and discrete ...
The likelihoood function of the Gaussian MA(1) zero-mean can be expressed in terms of the variance o...
The class of autoregressive (AR) processes is extensively used to model temporal dependence in obser...
The likelihood function for an autoregressive-moving average process is obtained using Kalman filter...
It was recently proved that any strictly stationary stochastic process can be viewed as an autoregre...
Assuming that the errors of an autoregressive process form a sequence of martingale differences, the...
2 We investigate autoregressive approximations of multiple frequency I(1), MFI(1), pro-cesses. The u...
The behaviour of the sample autocorrelation coefficients is important for the identification of the ...