Empirical results can be very dependent on model specification. How can analysts show model robustness in their research?
Model Uncertainty and Robustness: A Computational Framework for Multi-Model Analysis. Sociological Methods and Research. February 2017. Vol. 46(1): 3 – 40. (Lead article, with Katherine Holsteen)
Download: This do file installs our Stata program, loads in data sets, and replicates all the analyses in the paper.
OR: Paste the following command into Stata:
OR: type “ssc describe mrobust” in Stata
The multi-model analysis framework allows researchers to
(1) estimate thousands of regression models across combinations of control variables, functional forms, alternative variable definitions, and estimation commands;
(2) display the resulting modeling distribution of estimates; and
(3) discover which aspects of model specification have the most influence on a parameter estimate.
This allows analysts to clarify and demonstrate which modeling assumptions are essential to their empirical findings, and which are not.
NEW working paper:
“We Ran 9 Billion Regressions: Eliminating False Positives Through Computational Model Robustness.” 2017. With John Muñoz.
This is part of an enduring theme in my research, focusing on robust results. My earlier research:
Young, Cristobal. Model Uncertainty in Sociological Research: An Application to Religion and Economic Growth. American Sociological Review. June 2009.