Title: Panel-Corrected Standard Error Estimation in R
Version: 1.9.1.1
Date: 2018-06-07
Author: Delia Bailey <delia.bailey@gmail.com> and Jonathan N. Katz <jkatz@caltech.edu>
Maintainer: Delia Bailey <delia.bailey@gmail.com>
Description: A function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels.
License: GPL (≥ 3)
Packaged: 2018-06-07 18:47:40 UTC; delia.bailey
Repository: CRAN
Date/Publication: 2018-06-07 19:16:10 UTC
NeedsCompilation: no

Panel-Corrected Standard Error Estimation in R

Description

A function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels.

Details

The DESCRIPTION file:

Package: pcse
Title: Panel-Corrected Standard Error Estimation in R
Version: 1.9.1.1
Date: 2018-06-07
Author: Delia Bailey <delia.bailey@gmail.com> and Jonathan N. Katz <jkatz@caltech.edu>
Maintainer: Delia Bailey <delia.bailey@gmail.com>
Description: A function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels.
License: GPL (>= 3)
Packaged: 2018-04-03 13:42:19 UTC; hornik
Repository: CRAN
Date/Publication: 2018-04-03 13:52:56 UTC
NeedsCompilation: no

Index of help topics: 

agl                     agl
aglUn                   aglUn
pcse                    Panel-Corrected Standard Error Estimation in R
pcse-package            Panel-Corrected Standard Error Estimation in R
summary.pcse            Summary Method for Package pcse
vcovPC                  Extract Panel-Corrected Variance Covariance
                        Matrix

Further information is available in the following vignettes:

pcse Implementing Panel-Corrected Standard Errors in R: The pcse Package (source, pdf)

Author(s)

Delia Bailey <delia.bailey@gmail.com> and Jonathan N. Katz <jkatz@caltech.edu>

Maintainer: Delia Bailey <delia.bailey@gmail.com>

References

Alvarez, R. Michael, Geoffrey Garrett and Peter Lange. (1991) Government Partisanship, Labor Organization, and Macroeconomic Performance. American Political Science Review 85:539-56.

Beck, Nathaniel, Jonathan N. Katz, R. Michael Alvarez, Geoffrey Garrett and Peter Lange. (1993) Government Partisanship, Labor Organization, and Macroeconomic Performance: A Corrigendum. American Political Science Review 87:945-948.

Beck, Nathaniel and Jonathan N. Katz. (1995) What to do (and not to do) with Time-Series Cross-Section Data. American Political Science Review 89:634-647.

Bailey, Delia and Jonathan N. Katz. (2011) Implementing Panel-Corrected Standard Errors in R: The pcse Package. Journal of Statistical Software, Code Snippets 42(1):1–11. http://www.jstatsoft.org/v42/c01/

Examples

## see demo file.

agl

Description

Cross-National panel data on the OECD countries containing information about economic performance, government partisanship and labor organization.

Usage

data(agl)

Format

A data frame with 240 observations on the following 10 variables.

year

a numeric vector

country

a character vector

growth

the OECD growth rate

lagg1

An instrument for lagged growth rates constructed with an auxilary regression.

opengdp

weighted OECD demand

openex

weighted OECD export

openimp

weighted OECD import

leftc

"Left" cabinet composition

central

labor organization index

inter

interaction between leftc and central

Source

Alvarez, R. Michael, Geoffrey Garrett and Peter Lange. (1991) Government Partisanship, Labor Organization, and Macroeconomic Performance. American Political Science Review 85:539-56.

References

Beck, Nathaniel, Jonathan N. Katz, R. Michael Alvarez, Geoffrey Garrett and Peter Lange. (1993) Government Partisanship, Labor Organization, and Macroeconomic Performance: A Corrigendum. American Political Science Review 87:945-948.

Examples

data(agl)
summary(agl)

aglUn

Description

Cross-National panel data on the OECD countries containing information about economic performance, government partisanship and labor organization.

Usage

data(aglUn)

Format

A data frame with 230 observations on the following 10 variables.

year

a numeric vector

country

a character vector

growth

the OECD growth rate

lagg1

An instrument for lagged growth rates constructed with an auxilary regression.

opengdp

weighted OECD demand

openex

weighted OECD export

openimp

weighted OECD import

leftc

"Left" cabinet composition

central

labor organization index

inter

interaction between leftc and central

Details

This data frame differs from 'agl' only by the random omission of 10 rows of data. This is to created an unbalanced data version.

Source

Alvarez, R. Michael, Geoffrey Garrett and Peter Lange. (1991) Government Partisanship, Labor Organization, and Macroeconomic Performance. American Political Science Review 85:539-56.

References

Beck, Nathaniel, Jonathan N. Katz, R. Michael Alvarez, Geoffrey Garrett and Peter Lange. (1993) Government Partisanship, Labor Organization, and Macroeconomic Performance: A Corrigendum. American Political Science Review 87:945-948.

Examples

data(aglUn)
summary(aglUn)

Panel-Corrected Standard Error Estimation in R

Description

A function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels.

Usage

pcse(object, groupN, groupT, pairwise=FALSE)

Arguments

object

A lm object containing the initial run of OLS.

groupN

A vector containing the cross-sectional group identifier for each observation.

groupT

A vector containing the time identifier for each observation.

pairwise

An optional logical flag indicating whether the X's used to estimate the "middle" matrix should be chosen in a pairwise fashion or casewise fashion. If pairwise, the correlation between observations $i$ and $j$ is based on the time periods common to $i$ and $j$. If casewise, the correlation between observations i and j is based on the largest rectangular subset of the data, i.e., $T_i$ = $T_j$ = $T^*$ for all $i$ and $j$ if casewise is selected.

Examples

## see demo file.

Summary Method for Package pcse

Description

The package pcse contains a function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels. This function summarizes the estimated results.

Usage

## S3 method for class 'pcse'
summary(object, ...)

Arguments

object

An object of class "pcse."

...

Arguments passed to other functions.

Author(s)

Delia Bailey <delia.bailey@gmail.com> and Jonathan N. Katz <jkatz@caltech.edu> Maintainer: Delia Bailey <delia.bailey@gmail.com>

References

Bailey, Delia and Jonathan N. Katz. (2011) Implementing Panel-Corrected Standard Errors in R: The pcse Package. Journal of Statistical Software, Code Snippets 42(1):1–11. http://www.jstatsoft.org/v42/c01/

Examples

## see demo file.

Extract Panel-Corrected Variance Covariance Matrix

Description

The package pcse contains a function to estimate panel-corrected standard errors. Data may contain balanced or unbalanced panels. This function extracts the resulting variance covariance matrix.

Usage

vcovPC(x, ...)

## Default S3 method:
vcovPC(x, groupN, groupT, pairwise=FALSE, ...)

Arguments

x

A lm object containing the initial run of OLS.

groupN

A vector containing the cross-sectional group identifier for each observation.

groupT

A vector containing the time identifier for each observation.

pairwise

An optional logical flag indicating whether the X's used to estimate the "middle" matrix should be chosen in a pairwise fashion or casewise fashion. If pairwise, the correlation between observations $i$ and $j$ is based on the time periods common to $i$ and $j$. If casewise, the correlation between observations i and j is based on the largest rectangular subset of the data, i.e., $T_i$ = $T_j$ = $T^*$ for all $i$ and $j$ if casewise is selected.

...

Further arguments passed to methods.

Author(s)

Delia Bailey <delia.bailey@gmail.com> and Jonathan N. Katz <jkatz@caltech.edu> Maintainer: Delia Bailey <delia.bailey@gmail.com>

References

Bailey, Delia and Jonathan N. Katz. (2011) Implementing Panel-Corrected Standard Errors in R: The pcse Package. Journal of Statistical Software, Code Snippets 42(1):1–11. http://www.jstatsoft.org/v42/c01/

Examples

## see demo file.