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Implementing Convex Optimization in R: Two Econometric Examples.
- Source :
- Computational Economics; Dec2021, Vol. 58 Issue 4, p1127-1135, 9p
- Publication Year :
- 2021
-
Abstract
- Economists specify high-dimensional models to address heterogeneity in empirical studies with complex big data. Estimation of these models calls for optimization techniques to handle a large number of parameters. Convex problems can be effectively executed in modern programming languages. We complement Koenker and Mizera (J Stat Softw 60(5):1–23, 2014)'s work on numerical implementation of convex optimization, with focus on high-dimensional econometric estimators. Combining R and the convex solver MOSEK achieves speed gain and accuracy, demonstrated by examples from Su et al. (Econometrica 84(6):2215–2264, 2016) and Shi (J Econom 195(1):104–119, 2016). Robust performance of convex optimization is witnessed across platforms. The convenience and reliability of convex optimization in R make it easy to turn new ideas into executable estimators. [ABSTRACT FROM AUTHOR]
- Subjects :
- PROGRAMMING languages
MATHEMATICAL optimization
BIG data
MODERN languages
Subjects
Details
- Language :
- English
- ISSN :
- 09277099
- Volume :
- 58
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Computational Economics
- Publication Type :
- Academic Journal
- Accession number :
- 153556758
- Full Text :
- https://doi.org/10.1007/s10614-020-09995-z