fastplm-package: A package for fast fixed-effects algorithms.
In xuyiqing/fastplm: Fast Fixed Effects Algorithm
fastplm-package R Documentation
A package for fast fixed-effects algorithms.
Description
Fast Estimation of Linear Model and 2SLS Model with Multiple Fixed Effects
Details
This package performs fast estimation of multiple levels of fixed effects and various standard errors(clustered, jackknife and bootstrap).
Author(s)
Yiqing Xu(Maintainer); Minsheng Liu; Licheng Liu; Ziyi Liu
References
Anderson, T. W. and H. Rubin. 1949. Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, Vol. 20, pp. 46-63.
Angrist, J.D. and Pischke, J.-S. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton: Princeton University Press.
Cameron, A. C., & Miller, D. L. 2015. A practitioner’s guide to cluster-robust inference. Journal of human resources, 50(2), 317-372.
Correia, S. 2016. reghdfe: Estimating linear models with multi-way fixed effects. In 2016 Stata Conference (No. 24). Stata Users Group.
Correia, S. 2019. REGHDFE: Stata module to perform linear or instrumental-variable regression absorbing any number of high-dimensional fixed effects.
Davidson, R., & MacKinnon, J. G. 2010. Wild bootstrap tests for IV regression. Journal of Business & Economic Statistics, 28(1), 128-144.
Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. 2019. Fast and wild: Bootstrap inference in Stata using boottest. The Stata Journal, 19(1), 4-60.
Sanderson, E. and F. Windmeijer, 2015. A Weak Instrument F-Test in Linear IV Models with Multiple Endogenous Variables. Journal of Econometrics
See Also
fastplm
xuyiqing/fastplm documentation built on May 21, 2022, 5:39 a.m.
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| fastplm-package | R Documentation |
A package for fast fixed-effects algorithms.
Description
Fast Estimation of Linear Model and 2SLS Model with Multiple Fixed Effects
Details
This package performs fast estimation of multiple levels of fixed effects and various standard errors(clustered, jackknife and bootstrap).
Author(s)
Yiqing Xu(Maintainer); Minsheng Liu; Licheng Liu; Ziyi Liu
References
Anderson, T. W. and H. Rubin. 1949. Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, Vol. 20, pp. 46-63.
Angrist, J.D. and Pischke, J.-S. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton: Princeton University Press.
Cameron, A. C., & Miller, D. L. 2015. A practitioner’s guide to cluster-robust inference. Journal of human resources, 50(2), 317-372.
Correia, S. 2016. reghdfe: Estimating linear models with multi-way fixed effects. In 2016 Stata Conference (No. 24). Stata Users Group.
Correia, S. 2019. REGHDFE: Stata module to perform linear or instrumental-variable regression absorbing any number of high-dimensional fixed effects.
Davidson, R., & MacKinnon, J. G. 2010. Wild bootstrap tests for IV regression. Journal of Business & Economic Statistics, 28(1), 128-144.
Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. 2019. Fast and wild: Bootstrap inference in Stata using boottest. The Stata Journal, 19(1), 4-60.
Sanderson, E. and F. Windmeijer, 2015. A Weak Instrument F-Test in Linear IV Models with Multiple Endogenous Variables. Journal of Econometrics
See Also
fastplm
R Package Documentation
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