  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 statistical programming languages. We
complement Koenker and Mizera (2014)'s work on numerical implementation of
convex optimization, with focus on high-dimensional econometric estimators.
Combining R and the convex solver MOSEK achieves faster speed and equivalent
accuracy, demonstrated by examples from Su, Shi, and Phillips (2016) and Shi
(2016). Robust performance of convex optimization is witnessed cross platforms.
The convenience and reliability of convex optimization in R make it easy to
turn new ideas into prototypes.
