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Time series regression with unequally spaced data

Published online by Cambridge University Press:  14 July 2016

Richard H. Jones
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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.

Keywords

CORRELATED ERRORSSTATE SPACEKALMAN FILTER
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 

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