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Continuous Time Modelling Based on an Exact Discrete Time Representation

  • Marcus J. Chambers
  • J. Roderick McCrorie
  • Michael A. Thornton
Chapter

Abstract

This chapter provides a survey of methods of continuous time modelling based on an exact discrete time representation. It begins by highlighting the techniques involved with the derivation of an exact discrete time representation of an underlying continuous time model, providing specific details for a second-order linear system of stochastic differential equations. Issues of parameter identification, Granger causality, nonstationarity and mixed frequency data are addressed, all being important considerations in applications in economics and other disciplines. Although the focus is on Gaussian estimation of the exact discrete time model, alternative time domain (state space) and frequency domain approaches are also discussed. Computational issues are explored, and two new empirical applications are included along with a discussion of applications in the field of macroeconometric modelling.

Notes

Acknowledgements

We thank an editor and two anonymous referees for helpful comments that have led to improvements in this paper and the Scottish Institute for Research in Economics for arranging facilities in the School of Economics, University of Edinburgh, for the authors to meet to work on this chapter. The first author also thanks the Economic and Social Research Council for financial support under grant number ES/M01147X/1.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marcus J. Chambers
    • 1
  • J. Roderick McCrorie
    • 2
  • Michael A. Thornton
    • 3
  1. 1.Department of EconomicsUniversity of EssexEssexUK
  2. 2.School of Economics and FinanceUniversity of St. AndrewsSt. AndrewsUK
  3. 3.Department of Economics and Related StudiesUniversity of YorkYorkUK

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