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Annual Review of Economics

Volume 12, 2020

An Econometric Perspective on Algorithmic Subsampling

Abstract

Data sets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data, and diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which rows of data should we analyze, and might an arbitrary subset preserve the features of the original data? We review a line of work grounded in theoretical computer science and numerical linear algebra that finds that an algorithmically desirable sketch, which is a randomly chosen subset of the data, must preserve the eigenstructure of the data, a property known as subspace embedding. Building on this work, we study how prediction and inference can be affected by data sketching within a linear regression setup. We use statistical arguments to provide “inference-conscious” guides to the sketch size and show that an estimator that pools over different sketches can be nearly as efficient as the infeasible one using the full sample.

Keyword(s): coresets, countsketch, JEL C2, JEL C3, sketching, subspace embedding, uniform sampling
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/content/journals/10.1146/annurev-economics-022720-114138
2020-08-02
2025-03-25
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/content/journals/10.1146/annurev-economics-022720-114138
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An Econometric Perspective on Algorithmic Subsampling
Annual Review of Economics 12, 45 (2020); https://doi.org/10.1146/annurev-economics-022720-114138
/content/journals/10.1146/annurev-economics-022720-114138
/content/journals/10.1146/annurev-economics-022720-114138
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