Interpreting Results from a Two-Stage Least Squares Regression
Two-Stage Least Squares (2SLS) regression is a powerful econometric tool used when you suspect a problem of endogeneity—meaning one or more of your independent variables are correlated with the error term. This issue, common in non-experimental data (e.g., economics, finance, or social sciences), violates a core assumption of Ordinary Least Squares (OLS) and leads to biased and inconsistent coefficient estimates.
2SLS solves this by using instrumental variables (IVs) to isolate the exogenous variation in the endogenous variable. Interpreting the results, however, requires careful attention to both the statistical output and the validity of your instruments.
Step 1: Confirming the Need for 2SLS (Endogeneity Test)
Before interpreting the 2SLS results, you should statistically confirm that OLS would indeed be problematic.
- The Test: Use a test like the Hausman Test or a regression-based equivalent (like a Durbin-Wu-Hausman test).
- Interpretation:
- Null Hypothesis (): There is no systematic difference between the OLS and