Adaptive estimation in the linear random coefficients model when regressors have limited variation

Christophe Gaillac and Eric Gautier

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Abstract: We consider a linear model where the coecients - intercept and slopes - are random and independent from regressors which support is a proper subset. When the slopes do not have heavy tails, the joint density of the random coecients is identied. Lower bounds on the supremum risk for the estimation of the density are derived for this model and a related white noise model. We present an estimator, its rates of convergence, and a data-driven rule which delivers adaptive estimators.

Date: 2021
Note: View the original document on HAL open archive server: https://hal.science/hal-03374805
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Citations: View citations in EconPapers (2)

Published in Bernoulli, 2021, ⟨10.1007/s00041-021-09875-6⟩

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Related works:
Working Paper: Adaptive estimation in the linear random coefficients model when regressors have limited variation (2020)
Working Paper: Adaptive estimation in the linear random coefficients model when regressors have limited variation (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03374805

DOI: 10.1007/s00041-021-09875-6

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