Stata reports error when using ivreghdfe?

The error you encounter when using `ivreghdfe` in Stata typically stems from one of three core issues: a specification that violates the command's requirements, a data structure incompatible with its high-dimensional fixed effects absorption, or a conflict with the underlying `ivreg2` or `reghdfe` components. The most frequent cause is an incorrectly specified instrumental variable model where the number of excluded instruments is insufficient relative to the endogenous regressors, leading to underidentification, or where collinearity exists between the instruments, fixed effects, and other covariates after the absorption process. Other common triggers include the use of a `vce(robust)` or `vce(cluster clustvar)` option with singleton fixed effect groups, which creates missing standard errors, or attempting to absorb a factor variable that perfectly predicts the outcome or an instrument, resulting in a collinearity drop. Since `ivreghdfe` is a wrapper that merges the functionality of `ivreg2` with the absorption algorithms of `reghdfe`, errors often originate in these constituent commands and manifest as opaque return codes; understanding the specific error message is therefore critical.

Diagnosis requires a systematic approach beginning with the exact error text. Messages like "matrix has missing values" or "estimated covariance matrix of moment conditions not of full rank" directly point to identification problems, such as weak or redundant instruments. A "missing standard error" warning often indicates a clustering or robustness issue with the absorbed fixed effects. The first practical step is to run a simplified version of your model using the base `ivregress` or `ivreg2` command without fixed effects to verify your core IV logic and instrument validity. If that executes cleanly, the issue lies in the fixed effects absorption. Next, use `reghdfe` alone on a simple OLS version of your model to confirm your data and fixed effects structure are compatible with the absorption process—this can expose singleton groups or collinearity patterns. Examining the degrees of freedom reported by `ivreghdfe` in its output, or using the `verbose(1)` option, can reveal how many variables are being dropped during absorption, which is a key diagnostic for collinearity.

Resolution is contingent on the diagnosed root cause. For underidentification, you must source additional, valid instruments or reconsider your model specification. If the problem is collinearity introduced by the fixed effects, you may need to adjust the fixed effects structure, perhaps by interacting fewer dimensions or using a less saturated model. For singleton groups causing clustering errors, the `groupvar` option in `ivreghdfe` can sometimes be used to identify and address these groups, or you may need to adjust your clustering variable. In cases where the error persists, ensuring all packages (`ivreghdfe`, `reghdfe`, `ivreg2`, `ftools`) are updated to their latest versions is essential, as updates frequently address bugs and compatibility. As a last resort, manually partialling out the fixed effects using `reghdfe` with the `residuals` option for both the endogenous variables and instruments, and then performing a standard two-stage least squares regression on those residuals, can replicate the `ivreghdfe` process and often yields more transparent error messages at each stage, isolating the exact point of failure. This manual approach, while cumbersome, provides maximum transparency for debugging complex models with high-dimensional fixed effects.