Advice on using heteroskedasticity-based identification
By Christopher F. Baum, Arthur Lewbel. 2019
164 citations recorded by the source corpus.
Lewbel (2012, Journal of Business and Economic Statistics 30: 67–80) provides a heteroskedasticity-based estimator for linear regression models containing an endogenous regressor when no external instruments or other such information is available. The estimator is implemented in the command ivreg2h by Baum and Schaffer (2012, Statistical Software Components S457555, Department of Economics, Boston College). In this article, we give advice and instructions to researchers who want to use this estimator.
Subfields: Economics and Econometrics, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/57e62f7968f1942bda7a3753277f9d1aac280c13
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Citations in the corpus
- Omitted Variable Bias: Examining Management Research With the Impact Threshold of a Confounding Variable (ITCV) (2021, background): “For instance, Baum and Lewbel (2019) describe an increasingly pervasive technique that transforms heteroskedastic errors into appropriate instruments, and Le Gallo and Páez (2013) develop an approach that simulates instrumental variables that contain desirable properties.”
- How important is GVC participation to export upgrading?* (2021, methodology): “…are due to an unobserved common factor (Baum et al., 2013 p.13).6 Nevertheless, the method also allows the option of including external instruments, which Baum & Lewbel (2019) recommend to be adopted when there are external instruments in order to improve the efficiency of the IV estimator.”