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IDENTIFICATION IN MODELS WITH AUTOREGRESSIVE ERRORS
Journal of Econometrics, 1981Abstract Consider the model A(L)x t =B(L)y t +C(L)z t =u t , t=1,…,T , where A(L)=(B(L):C(L)) is a matrix of polynomials in the lag operator so that L r x t =x t−r , and y t is a vector of n endogenous variables, B(L)= ∑ s=0 k B s L s B 0 I
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Testing Normality in Autoregressive Models
Biometrika, 1985It is shown that any test of normality computed from autoregressive residuals has the same limiting null distribution as for the standard case of independent, identically distributed observations with estimated parameters. Some numerical results are given to indicate that this approximation is acceptable for sample size 20 in first- and second-order ...
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On the Autoregressive Model with Random Coefficients
Calcutta Statistical Association Bulletin, 1983The first and second order stationarity conditions for an autore-gressive model with random coefficients are obtained. In addition, for such a type of model, the asymptotic mean squared error of an h-step ahead forecast is also considered.
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NON‐NEGATIVE AUTOREGRESSIVE MODELS
Journal of Time Series Analysis, 1992Abstract. Consider a stationary non‐negative autoregressive (AR) model given xt=b1xt‐1, +…+bpxt‐p+et, where the et are independent identically distributed non‐negative variables and b1, …, bp are non‐negative parameters, and all the roots of the equation 1 –b1u–…–bpup= 0 are outside the unit circle.
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Missing Data in an Autoregressive Model
International Economic Review, 1974Sargan, J D, Drettakis, E G
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Unbounded heteroscedasticity in autoregressive models
The Journal of Economic Asymmetries, 2023Nikolaos Kourogenis +2 more
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On the Autoregressive Time Series Model Using Real and Complex Analysis
Forecasting, 2021Torsten Ullrich
exaly