Time Series Analysis of Irregularly Observed Data pp 276-289 | Cite as

# Multiple Time Series Analysis of Irregularly Spaced Data

## Abstract

Let Y(t) be a d-dimensional real column vector stochastic process, where t is scalar. We may define Y(t) in continuous time (t can take all real values) or discrete time (t = 0, ±δ, ±2δ,...). Either way Y(t) is measured within a finite interval *T* = [1,T] of t-values, but the full vector is not observed for all real t ε *T* (continuous case) or all discrete t ε *T* (discrete case). An important special case is discrete equally-spaced observation of a continuous process (see for example Bergstrom [1], Phillips [14], Robinson [16], Sargan [21]), which we discuss only insofar as the identifiability problem typically raised may be resolved by irregular sampling (see Section 3). A related case, which we do not discuss, is skip-sampling, where the discrete process defined above is recorded at intervals of kδ units, for integer k > 1 (for example, Robinson [16]).

## Keywords

Asymptotic Normality Time Series Model Block Diagonality Multiple Time Series Irregular Sampling## Preview

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