The Likelihood of a Continuous-time Vector Autoregressive Model
AbstractThis paper provides a method that weakens conditions under which the exact likelihood of a continuous-time vector autoregressive model can be derived. In particular, the method does not require the restrictions extant methods impose on discrete data that limit the applicability of continuous-time methods to real economic time series. The method applies generally to higher-order continuous-time systems involving mixed stock and flow data.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 419.
Date of creation: Oct 2000
Date of revision:
Continuous-time; Vector autoregression; Exact likelihood; Time series;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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- Bergstrom, Albert Rex, 1983. "Gaussian Estimation of Structural Parameters in Higher Order Continuous Time Dynamic Models," Econometrica, Econometric Society, vol. 51(1), pages 117-52, January.
- Bergstrom, A.R., 1997.
"Gaussian Estimation of Mixed-Order Continuous-Time Dynamic Models with Unobservable Stochastic Trends from Mixed Stock and Flow Data,"
Cambridge University Press, vol. 13(04), pages 467-505, August.
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- Chambers, Marcus J., 1999. "Discrete time representation of stationary and non-stationary continuous time systems," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 619-639, February.
- Bergstrom, A.R., 1984. "Continuous time stochastic models and issues of aggregation over time," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 20, pages 1145-1212 Elsevier.
- Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April.
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