Package: metafrontier 0.4.0

metafrontier: Analysis of Metafrontier Models for Efficiency and Productivity

Implements metafrontier production function models for estimating technical efficiencies and technology gaps for groups of firms that face different restrictions of a common underlying metatechnology (group-specific technologies in the sense of Battese, Rao, and O'Donnell, 2004). Supports both stochastic frontier analysis (SFA) and data envelopment analysis (DEA) based metafrontiers. Includes the deterministic metafrontier of Battese, Rao, and O'Donnell (2004) <doi:10.1023/B:PROD.0000012454.06094.29>, the stochastic metafrontier of Huang, Huang, and Liu (2014) <doi:10.1007/s11123-014-0402-2>, and the metafrontier Malmquist productivity index of O'Donnell, Rao, and Battese (2008) <doi:10.1007/s00181-007-0119-4>. The deterministic metafrontier can be identified by either the minimum sum of absolute deviations (LP) or the minimum sum of squared deviations (QP) criterion. Group frontiers can be production or cost frontiers, corrected for sample selection following Greene (2010) <doi:10.1007/s11123-009-0159-1>, or spatial-autoregressive stochastic frontiers. Transitive Fare-Primont and Lowe productivity indexes (O'Donnell, 2012 <doi:10.1093/ajae/aas023>) are decomposed against group technologies and the metatechnology. Additional features include panel SFA with time-varying inefficiency, bootstrap confidence intervals for technology gap ratios, a DEA poolability permutation test, latent class metafrontier estimation via the EM algorithm, Murphy-Topel corrected standard errors, convergence diagnostics, import of pre-fitted models from external estimation engines ('sfaR', 'frontier', 'Benchmarking'), and 'ggplot2' visualisation methods.

Authors:Erik Enstad [aut, cre]