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Implementing Convex Optimization in R: Two Econometric Examples
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.
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