arXiv 14 Apr 2024 · Econometrics
arXiv:2404.09309 · PDF · DOI · OpenAlex · Extracted main text
The julia package integrates the Julia programming language into Stata. Users can transfer data between Stata and Julia, issue Julia commands to analyze and plot, and pass results back to Stata. Julia's econometric ecosystem is not as mature as Stata's or R's or Python's. But Julia is an excellent environment for developing high-performance numerical applications, which can then be called from many platforms. For example, the boottest program for wild bootstrap-based inference (Roodman et al. 2019) and fwildclusterboot for R (Fischer and Roodman 2021) can use the same Julia back end. And the program reghdfejl mimics reghdfe (Correia 2016) in fitting linear models with high-dimensional fixed effects while calling a Julia package for tenfold acceleration on hard problems. reghdfejl also supports nonlinear fixed-effect models that cannot otherwise be fit in Stata--though preliminarily, as the Julia package for that purpose is immature.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Roodman, D., J. G. Mac\-Kinnon, M. . Nielsen, and M. D. Webb (2019) Fast and wild: Bootstrap inference in Stata using boottest self | 1.000 | 7 | 3 | 100% |
| 2 | Correia, S (2016) Linear models with high-dimensional fixed effects: An efficient and feasible estimator | 0.843 | 3 | 3 | 100% |
| 3 | Fischer, A., and D. Roodman (2021) fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Regression Models (Version 0.14.0). https://cran.r-project.or… | 0.644 | 2 | 2 | 100% |
| 4 | width30.25006ptheight2.62222ptdepth-2.25222pt (2013) Re-imagining a Stata/Python combination | 0.511 | 2 | 1 | 100% |
| 5 | Baum, C. F., M. E. Schaffer, and S. Stillman (2007) Enhanced routines for instrumental variables/GMM estimation and testing | 0.405 | 1 | 1 | 100% |
| 6 | Bergé, L (2018) Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm | 0.405 | 1 | 1 | 100% |
| 7 | Cameron, A. C., J. B. Gelbach, and D. L. Miller (2011) Robust inference with multiway clustering | 0.405 | 1 | 1 | 100% |
| 8 | Davidson, R., and J. G. Mac\-Kinnon (2010) Wild bootstrap tests for IV regression | 0.405 | 1 | 1 | 100% |
| 9 | Fiedler, J (2012) Imagining a Stata / Python combination | 0.405 | 1 | 1 | 100% |
| 10 | Fong, D. C.-L., and M. Saunders (2011) LSMR: An Iterative Algorithm for Sparse Least-Squares Problems | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 20 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Good Controls Gone Bad: Difference-in-Differences with Covariates | 0.405 | 1 | 1 |