Package index • plm

All functions
`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
`aneweytest()`	Angrist and Newey's version of Chamberlain test for fixed effects
`cipstest()`	Cross-sectionally Augmented IPS Test for Unit Roots in Panel Models
`cortab()`	Cross–sectional correlation matrix
`detect.lindep()` `alias(<plm>)` `alias(<pdata.frame>)`	Functions to detect linear dependence
`ercomp()` `print(<ercomp>)`	Estimation of the error components
`fixef(<plm>)` `print(<fixef>)` `summary(<fixef>)` `print(<summary.fixef>)` `fixef(<pggls>)`	Extract the Fixed Effects
`has.intercept()`	Check for the presence of an intercept in a formula or in a fitted model
`index(<pindex>)` `index(<pdata.frame>)` `index(<pseries>)` `index(<panelmodel>)`	Extract the indexes of panel data
`is.pbalanced()`	Check if data are balanced
`is.pconsecutive()`	Check if time periods are consecutive
`is.pseries()`	Check if an object is a pseries
`lead()` `lag(<pseries>)` `diff(<pseries>)`	lag, lead, and diff for panel data
`make.dummies()`	Create a Dummy Matrix
`make.pbalanced()`	Make data balanced
`make.pconsecutive()`	Make data consecutive (and, optionally, also balanced)
`model.frame(<pdata.frame>)` `formula(<pdata.frame>)` `model.matrix(<plm>)` `model.matrix(<pdata.frame>)`	model.frame and model.matrix for panel data
`mtest()`	Arellano–Bond Test of Serial Correlation
`nobs(<panelmodel>)` `nobs(<pgmm>)`	Extract Total Number of Observations Used in Estimated Panelmodel
`pFtest()`	F Test for Individual and/or Time Effects
`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
`pcce()` `summary(<pcce>)` `print(<summary.pcce>)` `residuals(<pcce>)` `model.matrix(<pcce>)` `pmodel.response(<pcce>)`	Common Correlated Effects estimators
`pcdtest()`	Tests of cross-section dependence for panel models
`pdata.frame()` `$<-`(<pdata.frame>) `[`(<pdata.frame>) `[[`(<pdata.frame>) `$`(<pdata.frame>) `print(<pdata.frame>)` `as.list(<pdata.frame>)` `as.data.frame(<pdata.frame>)`	pdata.frame: a data.frame for panel data
`pdim()` `print(<pdim>)`	Check for the Dimensions of the Panel
`pdwtest()`	Durbin–Watson Test for Panel Models
`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
`pgrangertest()`	Panel Granger (Non-)Causality Test (Dumitrescu/Hurlin (2012))
`phansitest()` `print(<phansitest>)`	Simes Test for unit roots in panel data
`pht()` `summary(<pht>)` `print(<summary.pht>)`	Hausman–Taylor Estimator for Panel Data
`phtest()`	Hausman Test for Panel Models
`piest()` `print(<piest>)` `summary(<piest>)` `print(<summary.piest>)`	Chamberlain estimator and test for fixed effects
`pldv()`	Panel estimators for limited dependent variables
`pvcovHC()` `plm.data()` `dynformula()` `formula(<dynformula>)` `print(<dynformula>)`	Deprecated functions of plm
`plm-package`	plm package: linear models for panel data
`plm()` `print(<plm.list>)` `terms(<panelmodel>)` `vcov(<panelmodel>)` `fitted(<panelmodel>)` `residuals(<panelmodel>)` `df.residual(<panelmodel>)` `coef(<panelmodel>)` `print(<panelmodel>)` `update(<panelmodel>)` `deviance(<panelmodel>)` `formula(<plm>)` `plot(<plm>)` `residuals(<plm>)` `fitted(<plm>)`	Panel Data Estimators
`plm.fast`	Option to Switch On/Off Fast Data Transformations
`plmtest()`	Lagrange FF Multiplier Tests for Panel Models
`pmg()` `summary(<pmg>)` `print(<summary.pmg>)` `residuals(<pmg>)`	Mean Groups (MG), Demeaned MG and CCE MG estimators
`pmodel.response()`	A function to extract the model.response
`pooltest()`	Test of Poolability
`predict(<plm>)`	Model Prediction for plm Objects
`print(<pseries>)` `as.matrix(<pseries>)` `plot(<pseries>)` `summary(<pseries>)` `plot(<summary.pseries>)` `print(<summary.pseries>)` `Sum()` `Between()` `between()` `Within()`	panel series
`pseriesfy()`	Turn all columns of a pdata.frame into class pseries.
`punbalancedness()`	Measures for Unbalancedness of Panel Data
`purtest()` `print(<purtest>)` `summary(<purtest>)` `print(<summary.purtest>)`	Unit root tests for panel data
`pvar()` `print(<pvar>)`	Check for Cross-Sectional and Time Variation
`pvcm()` `summary(<pvcm>)` `print(<summary.pvcm>)`	Variable Coefficients Models 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
`r.squared()`	R squared and adjusted R squared for panel models
`ranef(<plm>)`	Extract the Random Effects
`sargan()`	Hansen–Sargan Test of Overidentifying Restrictions
`summary(<plm.list>)` `coef(<summary.plm.list>)` `print(<summary.plm.list>)` `summary(<plm>)` `print(<summary.plm>)`	Summary for plm objects
`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(<pcce>)` `vcovHC(<pgmm>)`	Robust Covariance Matrix Estimators
`vcovNW()`	Newey and West (1987) Robust Covariance Matrix Estimator
`vcovSCC()`	Driscoll and Kraay (1998) Robust Covariance Matrix Estimator
`within_intercept()`	Overall Intercept for Within Models Along its Standard Error

Reference

All functions