Function reference • plm

Data data sets used to illustrate the use of mlogit
`Cigar`	Cigarette Consumption
`Crime`	Crime in North Carolina
`EmplUK`	Employment and Wages in the United Kingdom
`Gasoline`	Gasoline Consumption
`Grunfeld`	Grunfeld's Investment Data
`Hedonic`	Hedonic Prices of Census Tracts in the Boston Area
`LaborSupply`	Wages and Hours Worked
`Males`	Wages and Education of Young Males
`Parity`	Purchasing Power Parity and other parity relationships
`Produc`	US States Production
`RiceFarms`	Production of Rice in Indonesia
`Snmesp`	Employment and Wages in Spain
`SumHes`	The Penn World Table, v. 5
`Wages`	Panel Data of Individual Wages
Model estimation Estimation methods for panel data econometrics
`pcce()` `summary(<pcce>)` `print(<summary.pcce>)` `residuals(<pcce>)` `model.matrix(<pcce>)` `pmodel.response(<pcce>)`	Common Correlated Effects estimators
`pggls()` `summary(<pggls>)` `print(<summary.pggls>)` `residuals(<pggls>)`	General FGLS Estimators
`pgmm()` `coef(<pgmm>)` `summary(<pgmm>)` `print(<summary.pgmm>)`	Generalized Method of Moments (GMM) Estimation for Panel Data
`pldv()`	Panel estimators for limited dependent variables
`plm()` `terms(<panelmodel>)` `vcov(<panelmodel>)` `fitted(<panelmodel>)` `residuals(<panelmodel>)` `df.residual(<panelmodel>)` `coef(<panelmodel>)` `print(<panelmodel>)` `update(<panelmodel>)` `deviance(<panelmodel>)` `predict(<plm>)` `formula(<plm>)` `plot(<plm>)` `residuals(<plm>)` `fitted(<plm>)`	Panel Data Estimators
`pmg()` `summary(<pmg>)` `print(<summary.pmg>)` `residuals(<pmg>)`	Mean Groups (MG), Demeaned MG and CCE MG estimators
`pvcm()` `summary(<pvcm>)` `print(<summary.pvcm>)`	Variable Coefficients Models for Panel Data
Extractors Functions to extract elements of a fitted model
`nobs(<panelmodel>)` `nobs(<pgmm>)`	Extract Total Number of Observations Used in Estimated Panelmodel
`ranef(<plm>)`	Extract the Random Effects
`fixef(<plm>)` `print(<fixef>)` `summary(<fixef>)` `print(<summary.fixef>)`	Extract the Fixed Effects
`summary(<plm>)` `print(<summary.plm>)`	Summary for plm objects
`ercomp()` `print(<ercomp>)`	Estimation of the error components
`r.squared()`	R squared and adjusted R squared for panel models
Testing Test functions for panel data econometrics
`aneweytest()`	Chamberlain estimator and test for fixed effects
`cipstest()`	Cross-sectionally Augmented IPS Test for Unit Roots in Panel Models
`cortab()`	Cross--sectional correlation matrix
`mtest()`	Arellano--Bond test of Serial Correlation
`pbgtest()`	Breusch--Godfrey Test for Panel Models
`pbltest()`	Baltagi and Li Serial Dependence Test For Random Effects Models
`pbnftest()`	Modified BNF--Durbin--Watson Test and Baltagi--Wu's LBI Test for Panel Models
`pbsytest()`	Bera, Sosa-Escudero and Yoon Locally--Robust Lagrange Multiplier Tests for Panel Models and Joint Test by Baltagi and Li
`pcdtest()`	Tests of cross-section dependence for panel models
`pdwtest()`	Durbin--Watson Test for Panel Models
`pFtest()`	F Test for Individual and/or Time Effects
`pgrangertest()`	Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012))
`phtest()`	Hausman Test for Panel Models
`piest()` `print(<piest>)` `summary(<piest>)` `print(<summary.piest>)`	Chamberlain estimator and test for fixed effects
`plmtest()`	Lagrange FF Multiplier Tests for Panel Models
`pooltest()`	Test of Poolability
`purtest()` `print(<purtest>)` `summary(<purtest>)` `print(<summary.purtest>)`	Unit root tests for panel data
`pwaldtest()`	Wald-style Chi-square Test and F Test
`pwartest()`	Wooldridge Test for AR(1) Errors in FE Panel Models
`pwfdtest()`	Wooldridge first--difference--based test for AR(1) errors in levels or first--differenced panel models
`pwtest()`	Wooldridge's Test for Unobserved Effects in Panel Models
`sargan()`	Hansen--Sargan Test of Overidentifying Restrictions
vcov Robust covariance matrix estimators
`vcovBK()`	Beck and Katz Robust Covariance Matrix Estimators
`vcovDC()`	Double-Clustering Robust Covariance Matrix Estimator
`vcovG()`	Generic Lego building block for Robust Covariance Matrix Estimators
`vcovHC(<plm>)` `vcovHC(<pgmm>)`	Robust Covariance Matrix Estimators
`vcovNW()`	Newey and West (1987) Robust Covariance Matrix Estimator
`vcovSCC()`	Driscoll and Kraay (1998) Robust Covariance Matrix Estimator
Data management Enhanced data.frame and model components
`pdata.frame()` `$<-`(<pdata.frame>) `[`(<pdata.frame>) `[[`(<pdata.frame>) `$`(<pdata.frame>) `print(<pdata.frame>)` `as.list(<pdata.frame>)` `as.data.frame(<pdata.frame>)`	data.frame for panel data
`pmodel.response()`	A function to extract the model.response
`model.frame(<pdata.frame>)` `formula(<pdata.frame>)` `model.matrix(<plm>)` `model.matrix(<pdata.frame>)`	model.frame and model.matrix for panel data
`print(<pseries>)` `as.matrix(<pseries>)` `plot(<pseries>)` `summary(<pseries>)` `plot(<summary.pseries>)` `print(<summary.pseries>)` `Between()` `between()` `Within()`	panel series
`is.pbalanced()`	Check if data are balanced
`is.pconsecutive()`	Check if time periods are consecutive
`is.pseries()`	Check if an object is a pseries
`make.pbalanced()`	Make data balanced
`make.pconsecutive()`	Make data consecutive (and, optionally, also balanced)
`detect.lindep()` `alias(<plm>)` `alias(<pdata.frame>)`	Functions to detect linear dependence
`punbalancedness()`	Measures for Unbalancedness of Panel Data
`pvar()` `print(<pvar>)`	Check for Cross-Sectional and Time Variation
`index(<pindex>)` `index(<pdata.frame>)` `index(<pseries>)` `index(<panelmodel>)`	Extract the indexes of panel data
`nobs(<panelmodel>)` `nobs(<pgmm>)`	Extract Total Number of Observations Used in Estimated Panelmodel
`pdim()` `print(<pdim>)`	Check for the Dimensions of the Panel
`within_intercept()`	Overall Intercept for Within Models Along its Standard Error

Reference

Data

Model estimation

Extractors

Testing

vcov

Data management

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