Arbeitspapier
No Need to Run Millions of Regressions
We argue that in modelling cross-country growth models one should first identify so-called outlying observations. For the data set of Sala-i-Martin, we use the least median of squares (LMS) estimator to identify outliers. As LMS is not suited for inference, we then use reweighted least squares (RLS) for our cross-country growth models. We identify 27 variables that are significantly related to economic growth. Subsequently, applying Sala-i-Martin's approach for the data set without outliers hardly reveals any additional information. Variables that are insignificant according to the RLS method are generally not significantly related to economic growth under the Sala-i-Martin approach.
- Sprache
-
Englisch
- Erschienen in
-
Series: CESifo Working Paper ; No. 288
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Model Evaluation, Validation, and Selection
Economic Growth and Aggregate Productivity: General
- Thema
-
Sensitivity analysis
outliers
economic growth
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Sturm, Jan-Egbert
- Ereignis
-
Veröffentlichung
- (wer)
-
Center for Economic Studies and ifo Institute (CESifo)
- (wo)
-
Munich
- (wann)
-
2000
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Sturm, Jan-Egbert
- Center for Economic Studies and ifo Institute (CESifo)
Entstanden
- 2000
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