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Seeing is believing: Added-variable plots for complex estimators

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  • John Gallup

    (Portland State University, Department of Economics)

Abstract

Added-variable plots show the contribution of each data point of one explanatory variable to an outcome variable, while controlling for the influence of multiple other explanatory variables. This is a multivariate generalization of a scatterplot with a trend line. It provides an intuitive visual presentation of complex estimation results to specialists and nonspecialists alike. The plots show the marginal effect of an explanatory variable on the outcome as well as how closely the data adhere to the estimate. Observers can see outliers and the statistical significance of the estimated coefficient. The more complex the estimation method, the more helpful it is to have an accessible visual representation of the results. Currently, added-variable plots are available only for OLS regression in Stata. I recently extended the theory of added-variable plots to all commonly used linear and nonlinear estimators, including generalized least squares, instrumental variables, maximum likelihood, nonlinear least squares, and generalized method of moments estimators. I am in the process of programming added-variable plot commands for all Stata estimators. I have started with added-variable plots for panel data (xt) estimators (SJ 2020) and will shortly add them for instrumental variables and time-series estimators.

Suggested Citation

  • John Gallup, 2024. "Seeing is believing: Added-variable plots for complex estimators," 2024 Stata Conference 06, Stata Users Group.
    • Handle: RePEc:boc:usug24:06
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