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Can I read Analytic Derivatives of the Matrix Exponential for Estimation of Linear Continuous-time Models on EtoBox?
Analytic Derivatives of the Matrix Exponential for Estimation of Linear Continuous-time Models by Baoline Chen; Peter A. Zadrozny is a Economics, Econometrics and Finance article available to read on EtoBox.
What is Analytic Derivatives of the Matrix Exponential for Estimation of Linear Continuous-time Models about?
Linear-in-variables continuous-time processes are estimated nonlinearly, because the coe$cients of the implied linear-in-variables discrete-time estimating equations are the exponential of a matrix formed with the continuous-time parameters. Even with sampling complications such as irregular intervals, mixed frequencies, and stock and #ow variables, using Van loan's (1978) results, the mapping from continuous-to discrete-time parameters and its derivatives can be expressed as the submatrix of a matrix exponential. For quicker estimation and more accurate hypothesis testing or sensitivity analysis, it is often better to compute analytically the "rst-order derivatives of the mapping. This paper explains how to compute e$ciently the continuous-to discrete-time parameter mapping and its derivatives, without computing an eigenvalue decomposition, the common way of doing this. By linking present results with previous ones, a complete chain rule is obtained for computing the Gaussian likelihood function and its derivatives with respect to the continuous-time parameters.
Who reads Analytic Derivatives of the Matrix Exponential for Estimation of Linear Continuous-time Models?
It is typically read by researchers, students, and practitioners in Economics, Econometrics and Finance.
- Author
- Baoline Chen; Peter A. Zadrozny
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-1889)
- Published
- 2001
- Language
- EN
- Field
- Economics, Econometrics and Finance (Social Sciences)