Least Squares Estimation using Sketched Data with Heteroskedastic Errors
Authors: Sokbae Lee, Serena Ng
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we use Monte Carlo experiments to establish that when the errors are homoskedastic, estimates based on data sketched by random sampling or random projections will yield accurate inference. However, when the errors are heteroskedastic, sketching by random sampling will yield tests with size distortions, rejecting with much higher probability than the nominal size, unless robust standard errors are used. |
| Researcher Affiliation | Academia | 1Department of Economics, Columbia University, New York, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | An accompanying R package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=sketching and all replication files are available at https://github.com/sokbae/replication-Lee Ng-2022-ICML. |
| Open Datasets | Yes | We re-examine the OLS and 2SLS estimates of return to education in columns (1) and (2) of Table IV in Angrist & Krueger (1991). |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It describes generating data for Monte Carlo experiments and using a full sample for an empirical illustration without explicit partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'An accompanying R package is available...' but does not specify the version of R or any other software libraries used in the experiments. |
| Experiment Setup | Yes | Throughout the Monte Carlo experiment, we set n = 10^6, m = 500, and p = 6. There were 5,000 replications for each experiment. Six sketching methods are considered: (i) Bernoulli sampling, (ii) uniform sampling, (iii) leverage score sampling and reweighted regression as in Ma et al. (2020); (iv) countsketch, (v) SRHT, (vi) subsampled randomized Fourier transforms using the real part of fast discrete Fourier transform (SRFT). |