DeepAI AI Chat
Log In Sign Up

A Theoretical Analysis of the Stationarity of an Unrestricted Autoregression Process

by   Varsha S. Kulkarni, et al.

The higher dimensional autoregressive models would describe some of the econometric processes relatively generically if they incorporate the heterogeneity in dependence on times. This paper analyzes the stationarity of an autoregressive process of dimension k>1 having a sequence of coefficients β multiplied by successively increasing powers of 0<δ<1. The theorem gives the conditions of stationarity in simple relations between the coefficients and k in terms of δ. Computationally, the evidence of stationarity depends on the parameters. The choice of δ sets the bounds on β and the number of time lags for prediction of the model.


page 1

page 2

page 3

page 4


Consistency Results for Stationary Autoregressive Processes with Constrained Coefficients

We consider stationary autoregressive processes with coefficients restri...

Random autoregressive models: A structured overview

Models characterized by autoregressive structure and random coefficients...

Comments on the presence of serial correlation in the random coefficients of an autoregressive process

Through this note, we intend to show that the presence of serial correla...

Cointegration and representation of integrated autoregressive processes in function spaces

We provide a suitable generalization of cointegration for time series ta...

Margin-closed vector autoregressive time series models

Conditions are obtained for a Gaussian vector autoregressive time series...

Implicit Stacked Autoregressive Model for Video Prediction

Future frame prediction has been approached through two primary methods:...

Linear prediction of point process times and marks

In this paper, we are interested in linear prediction of a particular ki...