Article contents
Time series regression with unequally spaced data
Published online by Cambridge University Press: 14 July 2016
Abstract
Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.
- Type
- Part 2—Estimation for Time Series
- Information
- Journal of Applied Probability , Volume 23 , Issue A: Essays in Time Series and Allied Processes , 1986 , pp. 89 - 98
- Copyright
- Copyright © 1986 Applied Probability Trust
References
- 11
- Cited by