Title and Abstract
kinkyreg: Instrument-free inference for linear regression models with endogenous regressors
In models with endogenous regressors, a standard regression approach is to exploit just- or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other non-testable assumptions. While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg Stata program for kinky least squares inference that adopts an alternative approach to identification. By exploiting non-orthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The kinky least squares confidence bands can be more informative than confidence intervals obtained from instrumental variables estimation, in particular when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for standard instrumental variables inference. In particular, it allows to assess the validity of previously untestable just-identifying exclusion restrictions. Further instrument-free tests include linear hypotheses, functional form, heteroskedasticity, and serial correlation tests.
Kripfganz, S., and J. F. Kiviet (2020). kinkyreg: Instrument-free inference for linear regression models with endogenous regressors.
Proceedings of the 2020 UK Stata Conference.
University of Exeter
Jan F. Kiviet
University of Amsterdam;