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Empirical Research in Economics

  • Generalized linear models (GLMs) : Many standard microeconometric models belong to the family of generalized linear models and can be fitted by glm() from package stats. This includes in particular logit and probit models for modeling choice data and Poisson models for count data. Effects for typical values of regressors in these models can be obtained and visualized using effects. Marginal effects tables for certain GLMs can be obtained using the margins package. Interactive visualizations of both effects and marginal effects are possible in LinRegInteractive.
  • Binary responses : The standard logit and probit models (among many others) for binary responses are GLMs that can be estimated by glm() with family = binomial. Bias-reduced GLMs that are robust to complete and quasi-complete separation are provided by brglm. Discrete choice models estimated by simulated maximum likelihood are implemented in Rchoicebife provides binary choice models with fixed effects. Heteroscedastic probit models (and other heteroscedastic GLMs) are implemented in glmx along with parametric link functions and goodness-of-link tests for GLMs.
  • Count responses : The basic Poisson regression is a GLM that can be estimated by glm() with family = poisson as explained above. Negative binomial GLMs are available via glm.nb() in packageMASS. Another implementation of negative binomial models is provided by aod, which also contains other models for overdispersed data. Zero-inflated and hurdle count models are provided in package pscl. A reimplementation by the same authors is currently under development in countreg on R-Forge which also encompasses separate functions for zero-truncated regression, finite mixture models etc.
  • Multinomial responses : Multinomial models with individual-specific covariates only are available in multinom() from package nnet. An implementation with both individual- and choice-specific variables is mlogit. Generalized multinomial logit models (e.g., with random effects etc.) are in gmnl. A flexible framework of various customizable choice models (including multinomial logit and nested logit among many others) is implemented in the apollo package. Generalized additive models (GAMs) for multinomial responses can be fitted with the VGAM package. A Bayesian approach to multinomial probit models is provided by MNP. Various Bayesian multinomial models (including logit and probit) are available in bayesm. Furthermore, the package RSGHB fits various hierarchical Bayesian specifications based on direct specification of the likelihood function.
  • Ordered responses : Proportional-odds regression for ordered responses is implemented in polr() from package MASS. The package ordinal provides cumulative link models for ordered data which encompasses proportional odds models but also includes more general specifications. Bayesian ordered probit models are provided by bayesm.
  • Censored responses : Basic censored regression models (e.g., tobit models) can be fitted by survreg() in survival, a convenience interface tobit() is in package AER. Further censored regression models, including models for panel data, are provided in censReg. Censored regression models with conditional heteroscedasticity are in crch. Models for sample selection are available insampleSelection and semiparametric extensions of these are provided by SemiParSampleSel. Package matchingMarkets corrects for selection bias when the sample is the result of a stable matching process (e.g., a group formation or college admissions problem).