Identifying Restrictions for Finite Parameter Continuous Time Models with Discrete Time Data
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Other versions of this item:
- Blevins, Jason R., 2017. "Identifying Restrictions For Finite Parameter Continuous Time Models With Discrete Time Data," Econometric Theory, Cambridge University Press, vol. 33(3), pages 739-754, June.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Nail Kashaev & Natalia Lazzati, 2019. "Peer Effects in Random Consideration Sets," Papers 1904.06742, arXiv.org, revised May 2021.
- Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
- Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
- Hong, Han & Li, Weiming & Wang, Boyu, 2015. "Estimation of dynamic discrete models from time aggregated data," Journal of Econometrics, Elsevier, vol. 188(2), pages 435-446.
More about this item
Keywordsstochastic differential equations; identification; continuous time regression; Markov jump process; matrix exponential; matrix logarithm;
All these keywords.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ECM-2013-11-29 (Econometrics)
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