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Multiple Time Series Analysis of Irregularly Spaced Data

  • P. M. Robinson
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 25)

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1984

Authors and Affiliations

  • P. M. Robinson
    • 1
  1. 1.Department of MathematicsUniversity of SurreyUK

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