Posts Tagged ‘instrumental variables’

Estimation under omitted confounders, endogeneity, omitted variable bias, and related problems

Initial thoughts

Estimating causal relationships from data is one of the fundamental endeavors of researchers, but causality is elusive. In the presence of omitted confounders, endogeneity, omitted variables, or a misspecified model, estimates of predicted values and effects of interest are inconsistent; causality is obscured.

A controlled experiment to estimate causal relations is an alternative. Yet conducting a controlled experiment may be infeasible. Policy makers cannot randomize taxation, for example. In the absence of experimental data, an option is to use instrumental variables or a control function approach.

Stata has many built-in estimators to implement these potential solutions and tools to construct estimators for situations that are not covered by built-in estimators. Below I illustrate both possibilities for a linear model and, in a later post, will talk about nonlinear models. Read more…

Testing model specification and using the program version of gmm

This post was written jointly with Joerg Luedicke, Senior Social Scientist and Statistician, StataCorp.

The command gmm is used to estimate the parameters of a model using the generalized method of moments (GMM). GMM can be used to estimate the parameters of models that have more identification conditions than parameters, overidentified models. The specification of these models can be evaluated using Hansen’s J statistic (Hansen, 1982).

We use gmm to estimate the parameters of a Poisson model with an endogenous regressor. More instruments than regressors are available, so the model is overidentified. We then use estat overid to calculate Hansen’s J statistic and test the validity of the overidentification restrictions.

In previous posts Read more…