000			01399nam a22002297a 4500
003			ZW-GwMSU
005			20240327094218.0
008			240327b |||||||| |||| 00| 0 eng d
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHB139.T52 ECO
100	1		_aRobinson, Peter M. _eauthor
245	1	3	_aOn discrete sampling of time-varying continuous-time systems _cby Peter M. Robinson
264	1		_aCambridge : _bCambridge University Press, _c2009
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconometric Theory _vVolume 25, number 4
520			_aWe 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
650			_aStochastic process
856			_u10.1017/S0266466608090373
942			_2lcc _cJA
999			_c164578 _d164578