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Robust Inference Via Heteroskedasticity in Linear Models
We study inference via heteroskedasticity in linear models commonly used for macroeconomic policy analysis, where covariate endogeneity must often be addressed with limited time and data. Our framework nests standard heteroskedasticity-based approaches, allows for new non-nested restrictions, and does not require ex-ante regime labelling. We propose an easily implementable weak-identification-robust test and derive sufficient conditions for its validity. Simulation results show good size and power properties in a wide range of settings. Empirical applications to the fuel-price passthrough in Sierra Leone, the effect of remittances on consumption in the Philippines, and exchange-rate passthroughs in many countries illustrate the versatility and scalability of our approach.
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