On discrete sampling of time-varying continuous-time systems by Peter M. Robinson

By:

Robinson, Peter M [author]

Material type: Text

TextSeries: Econometric Theory ; Volume 25, number 4Cambridge : Cambridge University Press, 2009Content type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

Stochastic process

LOC classification:

HB139.T52 ECO

Online resources:

Click here to access online

Summary: 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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Item type	Current library	Call number	Vol info	Copy number	Status	Notes	Date due	Barcode
Journal Article	Main Library - Special Collections	HB139.T52 ECO (Browse shelf(Opens below))	vol. 25, no. 4 (985-994)	SP3259	Not for loan	For in house use

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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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