Title: Heteroscedasticity, Multiple Populations and Outliers in Trade Data
Authors: CERASA ANDREATORTI FrancescaPERROTTA Domenico
Publisher: Springer
Publication Year: 2016
JRC N°: JRC92590
ISBN: 978-3-319-44092-7
978-3-319-44093-4 (eBook)
ISSN: 2194-7767
2194-7775 (electronic)
URI: http://link.springer.com/chapter/10.1007/978-3-319-44093-4_5
http://publications.jrc.ec.europa.eu/repository/handle/JRC92590
DOI: 10.1007/978-3-319-44093-4_5
Type: Articles in periodicals and books
Abstract: International trade data are often affected by multiple linear populations and heteroscedasticity. An immediate consequence is the false declaration of outliers. We propose the monitoring of the White test statistic through the Forward Search as a new robust tool to test the presence of heteroscedasticity. We briefly describe how the regression estimates change when considering a heteroscedastic regression model. We finally show that, if the data are analyzed on a monthly basis, the heteroscedastic problem can be often bypassed.
JRC Directorate:Joint Research Centre Corporate Activities

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