Working Paper: NBER ID: w18859
Authors: Gary Solon; Steven J. Haider; Jeffrey Wooldridge
Abstract: The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting, and we suggest methods for appropriate inference.
Keywords: No keywords provided
JEL Codes: C1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| weighting for heteroskedasticity (C21) | improve precision of estimates (C51) |
| endogenous sampling (C90) | inconsistent parameter estimates (C51) |
| inverse probability weights (I14) | restore consistent estimation (C51) |
| weights reflecting population shares (F62) | inconsistent estimates of population average partial effects (C51) |