Robust inference via heteroskedasticity in linear models
研究了线性模型中利用异方差性进行推断的方法，适用于宏观经济政策分析中处理协变量内生性，提出了易于实施的弱识别稳健检验，并通过多个国家的实证案例展示了方法的通用性和可扩展性。
Summary 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 for 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.