pmg: Mean Groups (MG), Demeaned MG and CCE MG estimators

In plm: Linear Models for Panel Data

Description

Mean Groups (MG), Demeaned MG (DMG) and Common Correlated Effects MG (CCEMG) estimators for heterogeneous panel models, possibly with common factors (CCEMG)

Usage

pmg(
  formula,
  data,
  subset,
  na.action,
  model = c("mg", "cmg", "dmg"),
  index = NULL,
  trend = FALSE,
  ...
)

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

## S3 method for class 'summary.pmg'
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

## S3 method for class 'pmg'
residuals(object, ...)

Arguments

formula	a symbolic description of the model to be estimated,
data	a data.frame,
subset	see lm(),
na.action	see lm(),
model	one of "mg", "cmg", or "dmg",
index	the indexes, see pdata.frame(),
trend	logical specifying whether an individual-specific trend has to be included,
...	further arguments.
object, x	an object of class pmg,
digits	digits,
width	the maximum length of the lines in the print output,

Details

pmg is a function for the estimation of linear panel models with heterogeneous coefficients by various Mean Groups estimators. Setting argument model = "mg" specifies the standard Mean Groups estimator, based on the average of individual time series regressions. If model = "dmg" the data are demeaned cross-sectionally, which is believed to reduce the influence of common factors (and is akin to what is done in homogeneous panels when model = "within" and effect = "time"). Lastly, if model = "cmg" the CCEMG estimator is employed which is consistent under the hypothesis of unobserved common factors and idiosyncratic factor loadings; it works by augmenting the model by cross-sectional averages of the dependent variable and regressors in order to account for the common factors, and adding individual intercepts and possibly trends.

Value

An object of class c("pmg", "panelmodel") containing:

coefficients	the vector of coefficients,
residuals	the vector of residuals,
fitted.values	the vector of fitted values,
vcov	the covariance matrix of the coefficients,
df.residual	degrees of freedom of the residuals,
model	a data.frame containing the variables used for the estimation,
r.squared	numeric, the R squared,
call	the call,
indcoef	the matrix of individual coefficients from separate time series regressions.

Author(s)

Giovanni Millo

References

\insertRef

PESA:06plm

Examples

data("Produc", package = "plm")
## Mean Groups estimator
mgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc)
summary(mgmod)

## demeaned Mean Groups
dmgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, 
             data = Produc, model = "dmg")
summary(dmgmod)

## Common Correlated Effects Mean Groups
ccemgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, 
                data = Produc, model = "cmg")
summary(ccemgmod)

Example output

Loading required package: Formula
Mean Groups model
Call:
pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, 
    data = Produc)

Balanced Panel: n = 48, T = 17, N = 816

Residuals:
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0828079 -0.0118150  0.0004247  0.0000000  0.0126479  0.1189647 

Coefficients:
              Estimate Std. Error z-value  Pr(>|z|)    
(Intercept)  2.6722392  0.4126515  6.4758 9.433e-11 ***
log(pcap)   -0.1048507  0.0799132 -1.3121   0.18950    
log(pc)      0.2182539  0.0500862  4.3576 1.315e-05 ***
log(emp)     0.9334776  0.0750072 12.4452 < 2.2e-16 ***
unemp       -0.0037216  0.0016427 -2.2655   0.02348 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 849.81
Residual Sum of Squares: 0.33009
Multiple R-squared: 0.99961
Mean Groups model
Call:
pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, 
    data = Produc, model = "dmg")

Balanced Panel: n = 48, T = 17, N = 816

Residuals:
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0834415 -0.0076164 -0.0001226  0.0000000  0.0078109  0.1177009 

Coefficients:
              Estimate Std. Error z-value  Pr(>|z|)    
(Intercept)  0.0580979  0.1042881  0.5571  0.577466    
log(pcap)   -0.0629002  0.1021706 -0.6156  0.538133    
log(pc)      0.1607882  0.0591334  2.7191  0.006546 ** 
log(emp)     0.8425585  0.0704896 11.9529 < 2.2e-16 ***
unemp       -0.0050181  0.0020770 -2.4160  0.015693 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 849.81
Residual Sum of Squares: 0.23666
Multiple R-squared: 0.99972
Mean Groups model
Call:
pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, 
    data = Produc, model = "cmg")

Balanced Panel: n = 48, T = 17, N = 816

Residuals:
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0806338 -0.0037117  0.0003147  0.0000000  0.0040207  0.0438957 

Coefficients:
                Estimate Std. Error z-value  Pr(>|z|)    
(Intercept)   -0.6741754  1.0445518 -0.6454  0.518655    
log(pcap)      0.0899850  0.1176040  0.7652  0.444180    
log(pc)        0.0335784  0.0423362  0.7931  0.427698    
log(emp)       0.6258659  0.1071719  5.8398 5.225e-09 ***
unemp         -0.0031178  0.0014389 -2.1668  0.030249 *  
y.bar          1.0038005  0.1078874  9.3041 < 2.2e-16 ***
log(pcap).bar -0.0491919  0.2396185 -0.2053  0.837344    
log(pc).bar   -0.0033198  0.1576547 -0.0211  0.983200    
log(emp).bar  -0.6978359  0.2432887 -2.8683  0.004126 ** 
unemp.bar      0.0025544  0.0031848  0.8021  0.422505    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 849.81
Residual Sum of Squares: 0.056978
Multiple R-squared: 0.99993

plm documentation built on Sept. 21, 2021, 3:01 p.m.