With many thanks to Kit Baum package miinc has been uploaded to SSC.
miinc is a module to conduct multi-model inference using information criteria, akin to the R package MuMIn (http://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf).
Specifically, miinc implements Burnham and Anderson's (2002; 2004) multi-model inference framework for model averaging, model selection, and independent variable inclusion probability determination. Thus, miinc is very similar to the bma (SJ) and wals (SJ) commands but can fit many more estimation models.
miinc fits most single dependent variable/single equation modelsand saves results from the full model which allows it to work with postestimation commands for the base estimation command such as predict and margins, which are, of course, adjusted for model averaged coefficients/standard errors.
To install type
miinc requires Stata 12.1
references
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach. Springer.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference understanding AIC and BIC in model selection. Sociological methods & research, 33(2),
261-304.
- joe
miinc is a module to conduct multi-model inference using information criteria, akin to the R package MuMIn (http://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf).
Specifically, miinc implements Burnham and Anderson's (2002; 2004) multi-model inference framework for model averaging, model selection, and independent variable inclusion probability determination. Thus, miinc is very similar to the bma (SJ) and wals (SJ) commands but can fit many more estimation models.
miinc fits most single dependent variable/single equation modelsand saves results from the full model which allows it to work with postestimation commands for the base estimation command such as predict and margins, which are, of course, adjusted for model averaged coefficients/standard errors.
To install type
Code:
ssc install miinc
references
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach. Springer.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference understanding AIC and BIC in model selection. Sociological methods & research, 33(2),
261-304.
- joe
