Economics at your fingertips  

Implementing Convex Optimization in R: Two Econometric Examples

Zhan Gao and Zhentao Shi

Papers from

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 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.

Date: 2018-06, Revised 2019-08
New Economics Papers: this item is included in nep-big and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Latest version (application/pdf)

Related works:
Journal Article: Implementing Convex Optimization in R: Two Econometric Examples (2021) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Papers from
Bibliographic data for series maintained by arXiv administrators ().

Page updated 2023-06-15
Handle: RePEc:arx:papers:1806.10423