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. In particular, we replicate the simulation exercises in Su, Shi, and Phillips (2016) and Shi (2016) to show the robust performance of convex optimization cross platforms. Combining R and the convex solver MOSEK achieves faster speed and equivalent accuracy as in the original papers . The convenience and reliability of convex optimization in R make it easy to turn new ideas into prototypes.
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.
R
In particular, we replicate the simulation exercises in
Combining R and the convex solver MOSEK achieves faster speed and equivalent accuracy, demonstrated by examples from
R
to show the robust
. Robust
R
cross platforms. Combining R and the convex solver MOSEK achieves faster speed and equivalent accuracy as in the original papers
is witnessed cross platforms