OLS and IV estimation of regression models including endogenous interaction terms
| Autor: | Teresa D. Harrison, Maurice J.G. Bun |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Estimation
Statistics::Theory Economics and Econometrics jel:C31 05 social sciences Instrumental variable jel:C01 Endogeny Regression analysis endogeneity instrumental variables interaction term ordinary least squares 01 natural sciences jel:C36 Statistics::Machine Learning 010104 statistics & probability Nonlinear system 0502 economics and business Statistics Covariate Linear regression Econometrics Statistics::Methodology Endogeneity 0101 mathematics 050205 econometrics Mathematics |
| Zdroj: | Econometric Reviews. 38:814-827 |
| ISSN: | 1532-4168 0747-4938 |
| DOI: | 10.1080/07474938.2018.1427486 |
| Popis: | We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show that, under typical conditions regarding higher-order dependencies between endogenous and exogenous regressors, the OLS estimator of the coefficient of the interaction term is consistent and asymptotically normally distributed. Applying heteroskedasticity-consistent covariance matrix estimators, we then show that standard inference based on OLS is valid for the coefficient of the interaction term. Furthermore, we analyze several IV estimators, and show that an implementation assuming exogeneity of the interaction term is valid under fairly weak conditions. In the more general case, we derive that instruments need to be interacted with the exogenous part of the interaction to achieve identification. Finally, we propose several specification tests to empirically assess the validity of OLS and IV inference for the interaction model. Using our theoretical results we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth. |
| Databáze: | OpenAIRE |
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