k-Class Instrumental Variables Quantile Regression

No 2104, Working Papers from Department of Economics, University of Missouri

Abstract: With standard instrumental variables regression, k-class estimators have the potential to reduce bias, which is larger with weak instruments. With instrumental variables quantile regression, weak instrument-robust estimation is even more important because there is less guidance for assessing instrument strength. Motivated by this, we introduce an analogous k-class of estimators for instrumental variables quantile regression. We show the first-order asymptotic distribution under strong instruments is equivalent for all conventional choices of k. We evaluate finite-sample median bias in simulations. Computation is fast, and the "LIML" k reliably reduces median bias compared to the k=1 benchmark across a variety of data-generating processes, especially with greater degrees of overidentification. We also revisit some empirical estimates of consumption Euler equations. All code is provided online.

Keywords: bias; weak instruments (search for similar items in EconPapers)
JEL-codes: C21 C26 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ecm
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Journal Article: k-Class instrumental variables quantile regression (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:umc:wpaper:2104

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