EconPapers    
Economics at your fingertips  
 

Ordinary least squares and instrumental-variables estimators for any outcome and heterogeneity

Myoung-jae Lee and Chirok Han

Stata Journal, 2024, vol. 24, issue 1, 72-92

Abstract: Given an exogenous treatment d and covariates x, an ordinary least- squares (OLS) estimator is often applied with a noncontinuous outcome y to find the effect of d, despite the fact that the OLS linear model is invalid. Also, when d is endogenous with an instrument z, an instrumental-variables estimator (IVE) is often applied, again despite the invalid linear model. Furthermore, the treatment effect is likely to be heterogeneous, say, μ1(x), not a constant as assumed in most linear models. Given these problems, the question is then what kind of effect the OLS and IVE actually estimate. Under some restrictive conditions such as a “saturated model”, the estimated effect is known to be a weighted average, say, E{ω(x)μ1(x)}, but in general, OLS and the IVE applied to linear models with a noncontinuous outcome or heterogeneous effect fail to yield a weighted average of heterogeneous treatment effects. Recently, however, it has been found that E{ω(x)μ1(x)} can be estimated by OLS and the IVE without those restrictive conditions if the “propensity-score residual” d − E(d|x) or the “instrument-score residual” z−E(z|x) is used. In this article, we review this recent development and provide a command for OLS and the IVE with the propensity- and instrument-score residuals, which are applicable to any outcome and any heterogeneous effect.

Keywords: psr; OLS; IVE; propensity score; instrument score; overlap weight (search for similar items in EconPapers)
Date: 2024
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-1/st0740/
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1177/1536867X241233645

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:24:y:2024:i:1:p:72-92

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

DOI: 10.1177/1536867X241233645

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().

 
Page updated 2025-03-31
Handle: RePEc:tsj:stataj:v:24:y:2024:i:1:p:72-92