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.
| Original language | English |
|---|---|
| Title of host publication | Continuous Time Modeling in the Behavioral and Related Sciences |
| Editors | K van Montfort, J Oud, M Voelke |
| Publisher | Springer |
| Chapter | 14 |
| Pages | 317-357 |
| ISBN (Electronic) | 9783319772196 |
| ISBN (Print) | 9783319772189 |
| Publication status | Published - 13 Aug 2018 |
Keywords
- Continuous time
- Exact discrete time representation
- Stochastic differential equation
- Gaussian estimation
- Identification
- Granger causality
- Nonstationarity
- Mixed frequency data
- Computation
- Macroeconometric modelling