..oldfelm {lfe}

Fit a linear model with multiple group fixed effects (old interface)

Description

Fit a linear model with multiple group fixed effects (old interface)

Usage

..oldfelm(
  formula,
  data,
  exactDOF = FALSE,
  subset,
  na.action,
  contrasts = NULL,
  ...
)

Arguments

formula

an object of class '"formula"' (or one that can be coerced to that class): a symbolic description of the model to be fitted. Similarly to 'lm'. See Details.

data

a data frame containing the variables of the model.

exactDOF

logical. If more than two factors, the degrees of freedom used to scale the covariance matrix (and the standard errors) is normally estimated. Setting exactDOF=TRUE causes felm to attempt to compute it, but this may fail if there are too many levels in the factors. exactDOF='rM' will use the exact method in Matrix::rankMatrix(), but this is slower. If neither of these methods works, it is possible to specify exactDOF='mc', which utilizes a Monte-Carlo method to estimate the expectation E(x' P x) = tr(P), the trace of a certain projection, a method which may be more accurate than the default guess.

If the degrees of freedom for some reason are known, they can be specified like exactDOF=342772.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The 'factory-fresh' default is na.omit. Another possible value is NULL, no action. na.exclude is currently not supported.

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

...

other arguments.

• cmethod character. Which clustering method to use. Known arguments are 'cgm' (the default), 'cgm2' (or 'reghdfe', its alias). These alternate methods will generally yield equivalent results, except in the case of multiway clustering with few clusters along at least one dimension.

• keepX logical. To include a copy of the expanded data matrix in the return value, as needed by bccorr() and fevcov() for proper limited mobility bias correction.

• keepCX logical. Keep a copy of the centred expanded data matrix in the return value. As list elements cX for the explanatory variables, and cY for the outcome.

• keepModel logical. Keep a copy of the model frame.

• nostats logical. Don't include covariance matrices in the output, just the estimated coefficients and various descriptive information. For IV, nostats can be a logical vector of length 2, with the last value being used for the 1st stages.

• psdef logical. In case of multiway clustering, the method of Cameron, Gelbach and Miller may yield a non-definite variance matrix. Ordinarily this is forced to be semidefinite by setting negative eigenvalues to zero. Setting psdef=FALSE will switch off this adjustment. Since the variance estimator is asymptotically correct, this should only have an effect when the clustering factors have very few levels.

• kclass character. For use with instrumental variables. Use a k-class estimator rather than 2SLS/IV. Currently, the values ⁠'nagar', 'b2sls', 'mb2sls', 'liml'⁠ are accepted, where the names are from Kolesar et al (2014), as well as a numeric value for the 'k' in k-class. With kclass='liml', felm also accepts the argument ⁠fuller=<numeric>⁠, for using a Fuller adjustment of the liml-estimator.

• ⁠Nboot, bootexpr, bootcluster⁠ Since felm has quite a bit of overhead in the creation of the model matrix, if one wants confidence intervals for some function of the estimated parameters, it is possible to bootstrap internally in felm. That is, the model matrix is resampled Nboot times and estimated, and the bootexpr is evaluated inside an sapply. The estimated coefficients and the left hand side(s) are available by name. Any right hand side variable x is available by the name var.x. The "felm"-object for each estimation is available as est. If a bootcluster is specified as a factor, entire levels are resampled. bootcluster can also be a function with no arguments, it should return a vector of integers, the rows to use in the sample. It can also be the string 'model', in which case the cluster is taken from the model. bootexpr should be an expression, e.g. like quote(x/x2 * abs(x3)/mean(y)). It could be wise to specify nostats=TRUE when bootstrapping, unless the covariance matrices are needed in the bootstrap. If you need the covariance matrices in the full estimate, but not in the bootstrap, you can specify it in an attribute "boot" as nostats=structure(FALSE, boot=TRUE).

• ⁠iv, clustervar⁠ deprecated. These arguments will be removed at a later time, but are still supported in this field. Users are STRONGLY encouraged to use multipart formulas instead. In particular, not all functionality is supported with the deprecated syntax; iv-estimations actually run a lot faster if multipart formulas are used, due to new algorithms which I didn't bother to shoehorn in place for the deprecated syntax.