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On Discrete Sampling Of Time-Varying Continuous-Time Systems

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  • Robinson, Peter M.

Abstract

We consider a multivariate continuous-time process, generated by a system of linear stochastic differential equations, driven by white noise, and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing significantly simplifies matters). Such settings represent partial extensions of ones studied extensively by A.R. Bergstrom. A model for the observed time series is deduced. Initially we focus on a first-order model, but higher-order models are discussed in the case of equally-spaced observations. Some discussion of issues of statistical inference is included.

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  • Robinson, Peter M., 2009. "On Discrete Sampling Of Time-Varying Continuous-Time Systems," Econometric Theory, Cambridge University Press, vol. 25(4), pages 985-994, August.
    • Handle: RePEc:cup:etheor:v:25:y:2009:i:04:p:985-994_09
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    Cited by:

    1. Daniel L. Millimet & Ian K. McDonough, 2017. "Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 725-743, June.

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