Measuring influence in dynamic regression models

Peña, Daniel

Publication:
Measuring influence in dynamic regression models

dc.affiliation.dpto	UC3M. Departamento de Economía	es
dc.contributor.author	Peña, Daniel
dc.contributor.editor	Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned	2008-07-22T08:04:05Z
dc.date.available	2008-07-22T08:04:05Z
dc.date.issued	1990-06
dc.description.abstract	This article presents a methodology to build measures of influence in regression models with time series data. We introduce statistics that measure the influence of each observation on the parameter estimates and on the forecasts. These statistics take into account the autocorrelation of the sample. The first statistic can be decomposed to measure the change in the univariate ARIMA parameters, the transfer function parameters and the interaction between both. For independent data they reduce to the D statistics considered by Cook in the standard regression modelo These statistics can be easily computed using standard time series software. Their performance is analyzed in an example in which they seem to be useful to identify important events, such as additive outliers and trend shifts, in time series data.
dc.format.mimetype	application/pdf
dc.identifier.issn	2340-5031
dc.identifier.uri	https://hdl.handle.net/10016/2768
dc.language.iso	eng
dc.relation.ispartofseries	Working Papers
dc.relation.ispartofseries	1991-06
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRights	open access
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.eciencia	Economía
dc.subject.other	Missing observation Missing observations
dc.subject.other	Outliers
dc.subject.other	Intervention analysis
dc.subject.other	ARIMA models
dc.subject.other	Inverse autocorrelation function
dc.title	Measuring influence in dynamic regression models
dc.type	working paper	*
dspace.entity.type	Publication