Multiple outliers detection: application to research & development spending and productivity growth

Multiple outliers are frequently encountered in applied studies in business and economics. Most of the practitioners depend on ordinary least squares (OLS) method for parameter estimation in regression analysis without identifying outliers properly. It is evident that OLS totally fails even in prese...

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Detalles Bibliográficos
Autores principales:	Nurunnabi, A. A. M., Naser, Mohammed
Formato:	Artículo
Lenguaje:	English
Publicado:	BRAC University 2010
Materias:	Influential observation Masking Outlier Regression diagnostics Swamping
Acceso en línea:	http://hdl.handle.net/10361/421

id	10361-421
record_format	dspace
spelling	10361-4212019-09-29T05:47:23Z Multiple outliers detection: application to research & development spending and productivity growth Nurunnabi, A. A. M. Naser, Mohammed Influential observation Masking Outlier Regression diagnostics Swamping Multiple outliers are frequently encountered in applied studies in business and economics. Most of the practitioners depend on ordinary least squares (OLS) method for parameter estimation in regression analysis without identifying outliers properly. It is evident that OLS totally fails even in presence of single outlying observation. Single observation outlier detection methods are failed to numerically compare the sensitivity of the most popular diagnostic statistics. Data set from Griliches and Lichtenberg (1984) is used to show that we need to take extra care for model building process in presence of multiple outliers. 2010-10-10T06:48:59Z 2010-10-10T06:48:59Z 2008 Article http://hdl.handle.net/10361/421 en BRAC University Journal, BRAC University;Vol.5, No.2,pp. 31-39 application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Influential observation Masking Outlier Regression diagnostics Swamping
spellingShingle	Influential observation Masking Outlier Regression diagnostics Swamping Nurunnabi, A. A. M. Naser, Mohammed Multiple outliers detection: application to research & development spending and productivity growth
description	Multiple outliers are frequently encountered in applied studies in business and economics. Most of the practitioners depend on ordinary least squares (OLS) method for parameter estimation in regression analysis without identifying outliers properly. It is evident that OLS totally fails even in presence of single outlying observation. Single observation outlier detection methods are failed to numerically compare the sensitivity of the most popular diagnostic statistics. Data set from Griliches and Lichtenberg (1984) is used to show that we need to take extra care for model building process in presence of multiple outliers.
format	Article
author	Nurunnabi, A. A. M. Naser, Mohammed
author_facet	Nurunnabi, A. A. M. Naser, Mohammed
author_sort	Nurunnabi, A. A. M.
title	Multiple outliers detection: application to research & development spending and productivity growth
title_short	Multiple outliers detection: application to research & development spending and productivity growth
title_full	Multiple outliers detection: application to research & development spending and productivity growth
title_fullStr	Multiple outliers detection: application to research & development spending and productivity growth
title_full_unstemmed	Multiple outliers detection: application to research & development spending and productivity growth
title_sort	multiple outliers detection: application to research & development spending and productivity growth
publisher	BRAC University
publishDate	2010
url	http://hdl.handle.net/10361/421
work_keys_str_mv	AT nurunnabiaam multipleoutliersdetectionapplicationtoresearchdevelopmentspendingandproductivitygrowth AT nasermohammed multipleoutliersdetectionapplicationtoresearchdevelopmentspendingandproductivitygrowth
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Multiple outliers detection: application to research & development spending and productivity growth

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