Outliers

Abstract

Nearly all empirical investigations in economics, particularly those involving linear structural models or regressions, are subject to the problem of anomalous data, commonly called outliers. Roughly speaking, there are three sources of outliers. First, the distribution of the model’s random disturbances often has longer tails than the normal distribution, resulting in a greatly increased chance of larger disturbances. Second, the data set may contain erroneous numbers, or ‘gross errors’. The data bases most prone to gross errors are large cross sections, particularly those compiled from surveys; gross errors can result from misinterpreted questions, incorrectly recorded answers, keypunch errors, etc. Third, the model itself, typically linear in (transformations of) the variables, is only an approximation to reality. It is apt to be a poor representation of the process generating the data for extreme values of the explanatory variables. This source of outliers applies even to, say, macroeconomic time series, where the likelihood of gross errors is minimal.