Package: pdynmc
Type: Package
Title: Moment Condition Based Estimation of Linear Dynamic Panel Data
        Models
Version: 0.9.13.9009
Date: 2025-05-22
Authors@R: c(person("Markus", "Fritsch", role = c("aut", "cre"),
		email = "Markus.Fritsch@uni-Passau.de"),
	person("Joachim", "Schnurbus", role = c("aut"),
		email = "Joachim.Schnurbus@uni-Passau.de"),
	person("Andrew Adrian Yu", "Pua", role = c("aut"),
		email = "andrewypua@gmail.com"))
Depends: R (>= 3.6.0)
Imports: data.table (>= 1.12.2), MASS (>= 7.3-51.4), Matrix (>=
        1.2-17), methods(>= 3.6.2), optimx (>= 2018-07.10), stats (>=
        3.6.0), Rdpack (>= 0.11-0)
Suggests: pder (>= 1.0-1), testthat (>= 2.3.2), R.rsp (>= 0.43.2)
RdMacros: Rdpack
Description: Linear dynamic panel data modeling based on linear and
        nonlinear moment conditions as proposed by Holtz-Eakin, Newey,
        and Rosen (1988) <doi:10.2307/1913103>, Ahn and Schmidt (1995)
        <doi:10.1016/0304-4076(94)01641-C>, and Arellano and Bover
        (1995) <doi:10.1016/0304-4076(94)01642-D>. Estimation of the
        model parameters relies on the Generalized Method of Moments
        (GMM) and instrumental variables (IV) estimation, numerical
        optimization (when nonlinear moment conditions are employed)
        and the computation of closed form solutions (when estimation
        is based on linear moment conditions). One-step, two-step and
        iterated estimation is available. For inference and
        specification testing, Windmeijer (2005)
        <doi:10.1016/j.jeconom.2004.02.005> and doubly corrected
        standard errors (Hwang, Kang, Lee, 2021
        <doi:10.1016/j.jeconom.2020.09.010>) are available.
        Additionally, serial correlation tests, tests for
        overidentification, and Wald tests are provided. Functions for
        visualizing panel data structures and modeling results obtained
        from GMM estimation are also available. The plot methods
        include functions to plot unbalanced panel structure,
        coefficient ranges and coefficient paths across GMM iterations
        (the latter is implemented according to the plot shown in
        Hansen and Lee, 2021 <doi:10.3982/ECTA16274>). For a more
        detailed description of the GMM-based functionality, please see
        Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>. For
        more details on the IV-based estimation routines, see Fritsch,
        Pua, and Schnurbus (WP, 2024) and Han and Phillips (2010)
        <doi:10.1017/S026646660909063X>.
License: GPL (>=2)
URL: https://github.com/markusfritsch/pdynmc
BugReports: https://github.com/markusfritsch/pdynmc/issues
VignetteBuilder: R.rsp
Encoding: UTF-8
Classification/JEL: C23, C26, C87
RoxygenNote: 7.3.2
Config/pak/sysreqs: cmake
Repository: https://markusfritsch.r-universe.dev
Date/Publication: 2025-05-22 16:42:21 UTC
RemoteUrl: https://github.com/markusfritsch/pdynmc
RemoteRef: HEAD
RemoteSha: 3ea417f980af83e1b995d324728b5965a71150f0
NeedsCompilation: no
Packaged: 2026-07-16 07:34:45 UTC; root
Author: Markus Fritsch [aut, cre],
  Joachim Schnurbus [aut],
  Andrew Adrian Yu Pua [aut]
Maintainer: Markus Fritsch <Markus.Fritsch@uni-Passau.de>