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Identification and Estimation of Continuous Time Dynamic Systems With Exogenous Variables Using Panel Data

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  • Hamerle, Alfred
  • Singer, Hermann
  • Nagl, Willi

Abstract

This paper deals with the identification and maximum likelihood estimation of the parameters of a stochastic differential equation from discrete time sampling. Score function and maximum likelihood equations are derived explicitly. The stochastic differential equation system is extended to allow for random effects and the analysis of panel data. In addition, we investigate the identifiability of the continuous time parameters, in particular the impact of the inclusion of exogenous variables.

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  • Hamerle, Alfred & Singer, Hermann & Nagl, Willi, 1993. "Identification and Estimation of Continuous Time Dynamic Systems With Exogenous Variables Using Panel Data," Econometric Theory, Cambridge University Press, vol. 9(2), pages 283-295, April.
    • Handle: RePEc:cup:etheor:v:9:y:1993:i:02:p:283-295_00
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    Cited by:

    1. Rowińska, Paulina A. & Veraart, Almut E.D. & Gruet, Pierre, 2021. "A multi-factor approach to modelling the impact of wind energy on electricity spot prices," Energy Economics, Elsevier, vol. 104(C).
    2. 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.
    3. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    4. Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 199-215, June.

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