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

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  • Lee, Yoonseok
  • Sul, Donggyu

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.

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  • Lee, Yoonseok & Sul, Donggyu, 2023. "Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:jmvana:v:196:y:2023:i:c:s0047259x23000118
    DOI: 10.1016/j.jmva.2023.105165
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    References listed on IDEAS

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