Robust Inference Via Heteroskedasticity in Linear Models

Omer Faruk Akbal and Max-Sebastian Dovi

No 2026/100, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: Heteroskedasticity; weak identification; Anderson-Rubin; two-step inference (search for similar items in EconPapers)
Pages: 38
Date: 2026-05-22
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=576273 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2026/100

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().