Quantile Policy Effects: An Application to U.S. Macroprudential Policy

To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an invers...

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Bibliographic Details
Published in:Journal of business & economic statistics 2025-01, Vol.43 (1), p.81-97
Main Authors: Lin, Hsin-Yi, Hsiao, Yu-Hsiang, Hsu, Yu-Chin
Format: Article
Language:English
Subjects:
Bootstrap method
Causal effects
Economic policy
Impulse response functions
Macroeconomics
Macroprudential policy
Propensity score
Quantile functions
Time series
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Summary:To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for inference. Applying this method, we investigate the effectiveness of U.S. macroprudential actions on bank credit growth from 1948 to 2019. Empirically, we find that the effects of macroprudential policy on credit growth are asymmetric and depend on the quantiles of credit growth. The tightening of macroprudential actions fails to rein in high credit growth, whereas easing policies do not effectively stimulate bank credit growth during low-growth periods. These findings suggest that U.S. macroprudential policies might not sufficiently address the challenges of soaring bank credit or ensure overarching financial stability.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2024.2326140