AUTOREGRESSIVE PROCESSES WITH A TIME DEPENDENT VARIANCE
- 1 May 1982
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 3 (3) , 209-217
- https://doi.org/10.1111/j.1467-9892.1982.tb00343.x
Abstract
We study nonstationary autoregressive time series where the variance of the residual process is allowed to depend on time. In earlier publications the variance has been modelled by a step function. We look at more general classes of functions and propose two estimates of the autoregressive coefficients, both of which are consistent under weak assumptions. We also show how it is possible to obtain an estimate in practice using an iterative regression procedure.Keywords
This publication has 7 references indexed in Scilit:
- A Bayesian Robust Detection of Shift in the Risk Structure of Stock Market ReturnsJournal of the American Statistical Association, 1982
- The Identical Distribution Hypothesis for Stock Market Prices--Location- and Scale-Shift AlternativesJournal of the American Statistical Association, 1982
- Measuring deviations from stationarityStochastic Processes and their Applications, 1980
- Mixed autoregressive-moving average multivariate processes with time-dependent coefficientsJournal of Multivariate Analysis, 1978
- Tests for Variance Shift at an Unknown Time PointJournal of the Royal Statistical Society Series C: Applied Statistics, 1977
- Changes of Variance in First-Order Autoregressive Time Series Models-With an ApplicationJournal of the Royal Statistical Society Series C: Applied Statistics, 1976
- Some properties and examples of random processes that are almost wide sense stationaryIEEE Transactions on Information Theory, 1975