Enlarging of the sample to address multicollinearity

TL;DR

This paper examines how increasing sample size can reduce the effects of multicollinearity in statistical analysis, highlighting its limitations in addressing numerical instability, with practical examples in social sciences.

Contribution

It provides an analysis of sample enlargement as a method to mitigate multicollinearity, clarifying its effectiveness and limitations through empirical examples.

Findings

Sample enlargement can mitigate collinearity effects

It does not necessarily resolve numerical instability

Empirical examples illustrate the practical implications

Abstract

The paper analyzes how the enlarging of the sample affects to the mitigation of collinearity concluding that it may mitigate the consequences of collinearity related to statistical analysis but not necessarily the numerical instability. The problem that is addressed is of importance in the teaching of social sciences since it discusses one of the solutions proposed almost unanimously to solve the problem of multicollinearity. For a better understanding and illustration of the contribution of this paper, two empirical examples are presented and not highly technical developments are used.