Outliers and Spatial Dependence in Cross-Sectional Regressions

Bibliographic Data

ID4978426
AuthorsJesús Mur (0000-0002-7384-9166, Universidad de Zaragoza, corresponding author), Jørgen T Lauridsen (0000-0001-9889-6236, University of Southern Denmark, corresponding author), Jørgen Lauridsen (University of Southern Denmark)
Year2007
Volume39
Issue7
Pages1752-1769
Publication date2007-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning A Economy and Space (JOURNAL)
Journal identifiersISSN: 0308-518X • E-ISSN: 1472-3409
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1068/a38207
OpenAlexW2086187592
LanguageEN
Citations received1
References cited31

Outliers are a risk factor in any econometric analysis. They are often observations that exert an excessive influence on the results and lower our confidence in the estimations. As a consequence, the attention given to their identification and treatment in the context of time series is not surprising. Our intention in the present paper is to advance in this same direction but now focusing the discussion on the impact of outliers on cross-sectional specifications. In particular, we will analyse the behaviour of the most habitual misspecification tests in this field in the presence of outliers. With this objective, we present a series of analytical results that try to delimit the effects suffered by these statistics and complete the study with a Monte Carlo exercise designed to measure their effect more precisely. According to our results, the impact of outliers can be very important, especially when several coincide in the sample

Econometrics · Geography · Monte Carlo method · Outlier · Statistics · Computer Science · Efficiency Analysis Using DEA · Mathematics · Spatial and Panel Data Analysis · Urban Planning and Valuation