`vcovPC.Rd`

Estimation of sandwich covariances a la Beck and Katz (1995) for panel data.

vcovPC(x, cluster = NULL, order.by = NULL, pairwise = FALSE, sandwich = TRUE, fix = FALSE, ...) meatPC(x, cluster = NULL, order.by = NULL, pairwise = FALSE, kronecker = TRUE, ...)

x | a fitted model object. |
---|---|

cluster | a single variable indicating the clustering of observations,
or a |

order.by | a variable, list/data.frame, or formula indicating the
aggregation within time periods. By default |

pairwise | logical. For unbalanced panels. Indicating whether the meat should be estimated pair- or casewise. |

sandwich | logical. Should the sandwich estimator be computed?
If set to |

fix | logical. Should the covariance matrix be fixed to be positive semi-definite in case it is not? |

kronecker | logical. Calculate the meat via the
Kronecker-product, shortening the computation time for small
matrices. For large matrices, set |

... | arguments passed to the |

`vcovPC`

is a function for estimating Beck and Katz (1995)
panel-corrected covariance matrix.

The function `meatPC`

is the work horse for estimating
the meat of Beck and Katz (1995) covariance matrix estimators.
`vcovPC`

is a wrapper calling
`sandwich`

and `bread`

(Zeileis 2006).

Following Bailey and Katz (2011), there are two alternatives to
estimate the meat for unbalanced panels.
For `pairwise = FALSE`

, a balanced subset of the panel is used,
whereas for `pairwise = TRUE`

, a pairwise balanced sample is
employed.

The `cluster`

/`order.by`

specification can be made in a number of ways:
Either both can be a single variable or `cluster`

can be a
`list`

/`data.frame`

of two variables.
If `expand.model.frame`

works for the model object `x`

,
the `cluster`

(and potentially additionally `order.by`

) can also be
a `formula`

. By default (`cluster = NULL, order.by = NULL`

),
`attr(x, "cluster")`

and `attr(x, "order.by")`

are checked and
used if available. If not, every observation is assumed to be its own cluster,
and observations within clusters are assumed to be ordered accordingly.
If the number of observations in the model `x`

is smaller than in the
original `data`

due to `NA`

processing, then the same `NA`

processing
can be applied to `cluster`

if necessary (and `x$na.action`

being
available).

A matrix containing the covariance matrix estimate.

Bailey D, Katz JN (2011).
“Implementing Panel-Corrected Standard Errors in R: The pcse Package”,
*Journal of Statistical Software, Code Snippets*, **42**(1), 1--11.
doi: 10.18637/jss.v042.c01

Beck N, Katz JN (1995).
“What To Do (and Not To Do) with Time-Series-Cross-Section Data in Comparative Politics”,
*American Political Science Review*, **89**(3), 634--647.
doi: 10.2307/2082979

Zeileis A (2004).
“Econometric Computing with HC and HAC Covariance Matrix Estimator”,
*Journal of Statistical Software*, **11**(10), 1--17.
doi: 10.18637/jss.v011.i10

Zeileis A (2006).
“Object-Oriented Computation of Sandwich Estimators”,
*Journal of Statistical Software*, **16**(9), 1--16.
doi: 10.18637/jss.v016.i09

Zeileis A, Köll S, Graham N (2020).
“Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.”
*Journal of Statistical Software*, **95**(1), 1--36.
doi: 10.18637/jss.v095.i01

## Petersen's data data("PetersenCL", package = "sandwich") m <- lm(y ~ x, data = PetersenCL) ## Beck and Katz (1995) standard errors ## balanced panel sqrt(diag(vcovPC(m, cluster = ~ firm + year))) #> (Intercept) x #> 0.02220064 0.02527598 ## unbalanced panel PU <- subset(PetersenCL, !(firm == 1 & year == 10)) pu_lm <- lm(y ~ x, data = PU) sqrt(diag(vcovPC(pu_lm, cluster = ~ firm + year, pairwise = TRUE))) #> (Intercept) x #> 0.02206979 0.02533772 sqrt(diag(vcovPC(pu_lm, cluster = ~ firm + year, pairwise = FALSE))) #> (Intercept) x #> 0.02260277 0.02524119 # \donttest{ ## the following specifications of cluster/order.by are equivalent vcovPC(m, cluster = ~ firm + year) #> (Intercept) x #> (Intercept) 4.928685e-04 -4.396037e-05 #> x -4.396037e-05 6.388754e-04 vcovPC(m, cluster = PetersenCL[, c("firm", "year")]) #> (Intercept) x #> (Intercept) 4.928685e-04 -4.396037e-05 #> x -4.396037e-05 6.388754e-04 vcovPC(m, cluster = ~ firm, order.by = ~ year) #> (Intercept) x #> (Intercept) 4.928685e-04 -4.396037e-05 #> x -4.396037e-05 6.388754e-04 vcovPC(m, cluster = PetersenCL$firm, order.by = PetersenCL$year) #> (Intercept) x #> (Intercept) 4.928685e-04 -4.396037e-05 #> x -4.396037e-05 6.388754e-04 ## these are also the same when observations within each ## cluster are already ordered vcovPC(m, cluster = ~ firm) #> (Intercept) x #> (Intercept) 4.928685e-04 -4.396037e-05 #> x -4.396037e-05 6.388754e-04 vcovPC(m, cluster = PetersenCL$firm) #> (Intercept) x #> (Intercept) 4.928685e-04 -4.396037e-05 #> x -4.396037e-05 6.388754e-04 # }