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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.

Suggested Citation

  • 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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    1. Lee, Yoonseok, 2012. "Bias in dynamic panel models under time series misspecification," Journal of Econometrics, Elsevier, vol. 169(1), pages 54-60.
    2. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
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    5. Yoonseok Lee & Donggyu Sul, 2022. "Trimmed Mean Group Estimation," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 177-202, Emerald Group Publishing Limited.
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