Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels

Yoonseok Lee and Donggyu Sul ()

Journal of Multivariate Analysis, 2023, vol. 196, issue C

Abstract: We study the depth-weighted L-type location estimator of multivariate data when the observations are measured with noise. Under a drifting asymptotic framework, we show that the depth-weighted mean estimators with noisy data are still consistent and asymptotically mean-zero Gaussian under mild conditions. We apply the results to longitudinal data models of heterogeneous agents and develop the depth-weighted mean-group estimator of a vector of random coefficients, which estimates the multivariate average effect in heterogeneous panels or among heterogeneous treatment effects. As an empirical illustration, we examine the relative purchasing power parity.

Keywords: Depth; Heterogeneous panel; Longitudinal data; Multivariate average effect; Noisy data; Purchasing power parity; Random coefficient (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2023.105165

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