Time series regression with unequally spaced data
Journal of Applied ProbabilityPublished 1 January 1986
Richard H. Jones
Citations19
SJR quartileQ2
SJR score0.53
SNIP0.91
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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.
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Computer ScienceEngineering
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