IVREG2: Stata module for extended instrumental variables/2SLS and GMM estimation. ivreg2 provides extensions to Stata’s official ivregress and newey. Its main capabilities: two-step feasible GMM estimation; continuously updated GMM estimation (CUE); LIML and k-class estimation; automatic output of the Hansen-Sargan or Anderson-Rubin statistic for overidentifying restrictions; C statistic test of exogeneity of subsets of instruments (orthog() option); kernel-based autocorrelation-consistent (AC) and heteroskedastic and autocorrelation-consistent (HAC) estimation, with user-specified choice of kernel; Cragg’s ”heteroskedastic OLS” (HOLS) estimator; default reporting of large-sample statistics (z and chi-squared rather than t and F); small option to report small-sample statistics; first-stage regression reported with F-test of excluded instruments and R-squared with included instruments ”partialled-out”; enhanced Kleibergen-Paap and Cragg-Donald tests for weak instruments, redundancy of instruments, significance of endogenous regressors; two-way clustering of standard errors; Kiefer and Driscoll-Kraay standard errors. ivreg2 can also be used for ordinary least squares (OLS) estimation using the same command syntax as Stata’s official regress and newey. New in this version: ivreg2 now supports factor variables. This is version 4.1.11 of ivreg2, updated from that published in Stata Journal, 5(4), requiring Stata 11.2 or better. Stata 8.2/9.2/10.2 users may use this routine, which will automatically call ivreg28, ivreg29, or ivreg210, respectively. These versions are now included in the ivreg2 package. Stata 7 users may use the Stata Journal version of ivreg2, accessible via net search ivreg2.

References in zbMATH (referenced in 14 articles )

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  1. Auricchio, Marta; Ciani, Emanuele; Dalmazzo, Alberto; de Blasio, Guido: Redistributive public employment? A test for the South of Italy (2020)
  2. Hyunseung Kang, Yang Jiang, Qingyuan Zhao, Dylan S. Small: ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogenous Variable (2020) arXiv
  3. McKennan, Chris; Ober, Carole; Nicolae, Dan: Estimation and inference in metabolomics with nonrandom missing data and latent factors (2020)
  4. del Rosal, Ignacio: Export diversification and export performance by destination country (2019)
  5. Guo, Zijian; Kang, Hyunseung; Cai, T. Tony; Small, Dylan S.: Testing endogeneity with high dimensional covariates (2018)
  6. Kiviet, Jan F.: Discriminating between (in)valid external instruments and (in)valid exclusion restrictions (2017)
  7. Murray, Michael P.: Linear model IV estimation when instruments are many or weak (2017)
  8. Skeels, Christopher L.; Taylor, Larry W.: Prediction after IV estimation (2014)
  9. Casado, Esteban; Ferrer, Juan-Carlos: Consumer price sensitivity in the retail industry: latitude of acceptance with heterogeneous demand (2013)
  10. Lin, Eric S.; Chou, Ta-Sheng: A note on Bayesian interpretations of HCCME-type refinements for nonlinear GMM models (2012)
  11. Palmer, Tom M.; Lawlor, Debbie A.; Harbord, Roger M.; Sheehan, Nuala A.; Tobias, Jon H.; Timpson, Nicholas J.; Smith, George Davey; Sterne, Jonathan Ac: Using multiple genetic variants as instrumental variables for modifiable risk factors (2012)
  12. Millimet, Daniel L.; Collier, Trevor: Efficiency in public schools: does competition matter? (2008)
  13. Oulton, Nicholas: Chain indices of the cost-of-living and the path-dependence problem: an empirical solution (2008)
  14. Temple, Jonathan; Wößmann, Ludger: Dualism and cross-country growth regressions (2006)