Maximum Likelihood Estimation: A Prediction Error Approach

Ralph D. Snyder

No 267131, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper the problem of computing maximum likelihood estimates of the parameters of linear statistical models is considered. The proposed approach relies on the prediction error decomposition of the likelihood function. A distinctive feature is that the required prediction errors are obtained using conventional linear least squares methods rather than the more usual Kalman filter. More specifically, it is shown that the orthogonalization procedure based on fast Givens transformations, used to obtain the triangular representation of the normal equations, automatically yields the one-step ahead prediction errors and their mean squared errors without additional side calculations.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 14
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/267131/files/monash-106.pdf (application/pdf)
https://ageconsearch.umn.edu/record/267131/files/monash-106.pdf?subformat=pdfa (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267131

DOI: 10.22004/ag.econ.267131

Access Statistics for this paper

More papers in Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().