Continuous Time ARMA Processes: Discrete Time Representation and Likelihood Evaluation

Michael Thornton and Marcus Chambers

Discussion Papers from Department of Economics, University of York

Abstract: This paper explores the representation and estimation of mixed continuous time ARMA (autoregressive moving average) systems of orders p, q. Taking the general case of mixed stock and flow variables, we discuss new state space and exact discrete time representations and demonstrate that the discrete time ARMA representations widely used in empirical work, based on differencing stock variables, are members of a class of observationally equivalent discrete time ARMA(p + 1, p) representations, which includes a more natural ARMA(p, p) representation. We compare and contrast two approaches to likelihood evaluation and computation, namely one based on an exact discrete time representation and another utilising astate space representation and the Kalman-Bucy filter.

Keywords: Continuous time; ARMA process; state space; discrete time representation. (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Date: 2016-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (3)

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Journal Article: Continuous time ARMA processes: Discrete time representation and likelihood evaluation (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:yorken:16/10

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