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Automatic Inference for Value-Added Regressions

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  • Tian Xie

Abstract

It is common to use shrinkage methods such as empirical Bayes to improve estimates of teacher value-added. However, when the goal is to perform inference on coefficients in the regression of long-term outcomes on value-added, it's unclear whether shrinking the value-added estimators can help or hurt. In this paper, we consider a general class of value-added estimators and the properties of their corresponding regression coefficients. Our main finding is that regressing long-term outcomes on shrinkage estimates of value-added performs an automatic bias correction: the associated regression estimator is asymptotically unbiased, asymptotically normal, and efficient in the sense that it is asymptotically equivalent to regressing on the true (latent) value-added. Further, OLS standard errors from regressing on shrinkage estimates are consistent. As such, efficient inference is easy for practitioners to implement: simply regress outcomes on shrinkage estimates of value added.

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  • Tian Xie, 2025. "Automatic Inference for Value-Added Regressions," Papers 2503.19178, arXiv.org.
    • Handle: RePEc:arx:papers:2503.19178
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    File URL: http://arxiv.org/pdf/2503.19178
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