No Need to Run Millions of Regressions

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 288

Classification and Topics

• Classification: 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
• Subject: Sensitivity analysis ; outliers ; economic growth

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Sturm, Jan-Egbert
• Event: Veröffentlichung
• (who): Center for Economic Studies and ifo Institute (CESifo)
• (where): Munich
• (when): 2000

Further information

• Handle: http://hdl.handle.net/10419/75605
• Last update: 10.03.2025, 11:44 AM CET