• Corpus ID: 88522142

Estimating the Variance of Measurement Errors in Running Variables of Sharp Regression Discontinuity Designs

@article{Mori2017EstimatingTV,
  title={Estimating the Variance of Measurement Errors in Running Variables of Sharp Regression Discontinuity Designs},
  author={Kota Mori},
  journal={arXiv: Methodology},
  year={2017}
}
  • Kota Mori
  • Published 18 September 2017
  • Mathematics
  • arXiv: Methodology
Treatment effect estimation through regression discontinuity designs faces a severe challenge when the running variable is measured with errors, as the errors smooth out the discontinuity that the identification hinges on. Recent studies have shown that the variance of the measurement error term plays an important role on both bias correction and identification under such situations, but little is studied about how one can estimate the unknown variance from data. This paper proposes two… 

Figures from this paper

Improving Power System State Estimation Based on Matrix-Level Cleaning

TLDR
This paper proposes a data-driven approach to clean measurement error in matrix-level based on random matrix theory, and significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS.

References

SHOWING 1-9 OF 9 REFERENCES

The Effect of Measurement Error in the Sharp Regression Discontinuity Design

This paper develops a nonparametric analysis for the sharp regression discontinuity (RD) design in which the continuous forcing variable may contain measurement error. We show that if the observable

The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the

Large sample estimation and hypothesis testing

Pattern Recognition and Machine Learning

TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.

Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the

R: A language and environment for statistical computing.

Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice

Estimation and Inference in Two-Step Econometric Models

A commonly used procedure in a wide class of impirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails

Pattern Recognition: Introduction to Unsupervised Learning (in Japanese)

  • Ohmsha.
  • 2014