Influence analysis with panel data using Stata

Session

Diagnostic plots (such as leverage-versus-squared residual plots) and measures of overall influence (for example, Cook's [1979] distance) are usually used to detect such anomalies, but there are two different problems arising from their use. First, available commands for diagnostic plots are built for cross-sectional data, and some data manipulation is necessary for panel data. Second, Cook-like distances may fail to flag multiple anomalous cases in the data because they do not account for pairwise influence of observations (Atkinson 1993; Chatterjee and Hadi 1988, Rousseeuw 1991; Rousseeuw and Van Zomeren 1990, Lawrance 1995). I overcome these limits as follows. First, I formalize statistical measures to quantify the degree of leverage and outlyingness of units in a panel-data framework to produce diagnostic plots suitable for panel data. Second, I build on Lawrance's [1995] pairwise approach by proposing measures for joint and conditional influence suitable for panel-data models with fixed effects.

I develop a method to visually detect anomalous units in a panel dataset and identify their types; investigate the effect of these units on LS estimates, and on other units’ influence. I propose two community-contributed commands in Stata to implement this method. xtlvr2plot produces a leverage-versus-residual plot suitable for panel data, and a summary table with the list of detected anomalous units and their type. xtinfluencecalculates the joint and conditional influence and effects of pairs of units, and generates network-style plots (an option between scatterplot or heat plot is allowed by the command).

JEL codes: C13, C15, C23.