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pdynmc
View on CRAN: Click
here
Download and install pdynmc package within the R console
Install from CRAN:
install.packages("pdynmc")
Install from Github:
library("remotes")
install_github("cran/pdynmc")
Install by package version:
library("remotes")
install_version("pdynmc", "0.9.12")
Attach the package and use:
library("pdynmc")
Maintained by
Markus Fritsch
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.pdynmc . https://github.com/markusfritsch/pdynmc/issues . https://github.com/markusfritsch/pdynmc . pdynmc citation info . pdynmc results . pdynmc.pdf . pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions . pdynmc – An R-Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions . R-package pdynmc: GMM Estimation of Dynamic Panel Data Models Based on Nonlinear Moment Conditions . pdynmc_0.9.12.tar.gz . pdynmc_0.9.12.zip . pdynmc_0.9.12.zip . pdynmc_0.9.12.zip . pdynmc_0.9.12.tgz . pdynmc_0.9.12.tgz . pdynmc_0.9.12.tgz . pdynmc_0.9.12.tgz . pdynmc_0.9.12.tgz . pdynmc_0.9.12.tgz . pdynmc archive . https://CRAN.R-project.org/package=pdynmc .
First Published: 2020-02-01
Latest Update: 2023-11-24
Description:
Linear dynamic panel data modeling based on linear and
nonlinear moment conditions as proposed by
Holtz-Eakin, Newey, and Rosen (1988) ,
Ahn and Schmidt (1995) ,
and Arellano and Bover (1995) .
Estimation of the model parameters relies on the Generalized
Method of Moments (GMM), 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)
and doubly corrected standard errors
(Hwang, Kang, Lee, 2021 )
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 ).
For a more detailed description of the functionality, please
see Fritsch, Pua, Schnurbus (2021) .
How to cite:
Markus Fritsch (2020). pdynmc: Moment Condition Based Estimation of Linear Dynamic Panel Data Models. R package version 0.9.12, https://cran.r-project.org/web/packages/pdynmc. Accessed 11 Apr. 2025.
Previous versions and publish date:
0.8.0 (2020-02-01 11:40), 0.9.0 (2020-05-07 12:30), 0.9.1 (2020-07-27 01:10), 0.9.2 (2020-09-17 01:10), 0.9.3 (2020-12-05 00:40), 0.9.4 (2021-06-14 15:50), 0.9.5 (2021-08-13 16:00), 0.9.6 (2021-10-14 15:00), 0.9.7 (2022-03-24 23:00), 0.9.8 (2022-08-30 18:40), 0.9.9 (2023-06-06 14:30), 0.9.10 (2023-11-24 23:10), 0.9.11 (2024-07-12 17:30)
Other packages that cited pdynmc R package
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Other R packages that pdynmc depends,
imports, suggests or enhances
Complete documentation for pdynmc
Functions, R codes and Examples using
the pdynmc R package
Some associated functions: ABdata . case.names.pdynmc . cigDemand . coef.pdynmc . data.info . dummy.coef.pdynmc . fitted.pdynmc . jtest.fct . model.matrix.pdynmc . mtest.fct . ninst . ninst.pdynmc . nobs.pdynmc . optimIn . optimIn.pdynmc . pDensTime.plot . package-pdynmc . pdynmc . plot.pdynmc . print.pdynmc . print.summary.pdynmc . residuals.pdynmc . strucUPD.plot . summary.pdynmc . variable.names.pdynmc . vcov.pdynmc . wald.fct . wmat . wmat.pdynmc .
Some associated R codes: ABdata.R . cigDemand.R . globals.R . package-pdynmc.R . pdynmc_estFct.R . pdynmc_exploratory.R . pdynmc_fitMethods.R . pdynmc_furtherHelperFcts.R . pdynmc_instMatFcts.R . pdynmc_specTestFcst.R . Full pdynmc package functions and examples
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