An Econometric Perspective on Algorithmic Subsampling

Sokbae (Simon) Lee and Serena Ng ()

Annual Review of Economics, 2020, vol. 12, issue 1, 45-80

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.

Date: 2020
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Citations: View citations in EconPapers (7)

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Working Paper: An Econometric Perspective on Algorithmic Subsampling (2020)
Working Paper: An econometric perspective on algorithmic subsampling (2020)
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DOI: 10.1146/annurev-economics-022720-114138

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