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Implementing Convex Optimization in R: Two Econometric Examples

Zhan Gao () and Zhentao Shi
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Zhan Gao: University of Southern California

Computational Economics, 2021, vol. 58, issue 4, No 8, 1127-1135

Abstract: 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.

Keywords: Big data; Convex optimization; High-dimensional model; Numerical solver (search for similar items in EconPapers)
JEL-codes: C13 C55 C61 C87 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10614-020-09995-z

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