A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new overidentifying restriction test for high-dimensional instrumental variable models, even when data exceeds sample size. The novel test offers enhanced power and robustness for complex economic analyses.
Area Of Science
- Econometrics
- Statistics
Background
- High-dimensional instrumental variable (IV) models are crucial for causal inference in econometrics.
- Existing overidentifying restriction tests often fail when the number of covariates and instruments exceeds the sample size.
Purpose Of The Study
- To propose a novel overidentifying restriction test for high-dimensional linear IV models.
- To develop a test that accommodates more covariates and instruments than the sample size.
- To enhance the power and robustness of existing tests in challenging data scenarios.
Main Methods
- The proposed test utilizes a maximum norm approach for high-dimensional parameters.
- A power-enhanced version is introduced, incorporating an asymptotically zero component.
- The test is designed to be scale-invariant and robust to heteroskedastic errors.
Main Results
- The maximum norm test demonstrates higher theoretical power than the modified Cragg-Donald test for large-dimensional covariates.
- The power-enhanced test improves detection of extreme alternatives, particularly with numerous locally invalid instruments.
- The test's practical utility is validated through an empirical example on trade and economic growth.
Conclusions
- The developed overidentifying restriction test is effective for high-dimensional IV models.
- The test provides a valuable tool for causal inference in econometrics with large datasets.
- The findings contribute to robust statistical inference in complex economic modeling.
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