fdm {panelr}

Estimate first differences models using GLS

Description

The function fits first difference models using GLS estimation.

Usage

fdm(
  formula,
  data,
  id = NULL,
  wave = NULL,
  use.wave = FALSE,
  min.waves = 1,
  variance = c("toeplitz-1", "constrained", "unconstrained"),
  error.type = c("CR2", "CR1S"),
  ...
)

Arguments

formula

Model formula. See details for crucial info on panelr's formula syntax.

data

The data, either a panel_data object or data.frame.

id

If data is not a panel_data object, then the name of the individual id column as a string. Otherwise, leave as NULL, the default.

wave

If data is not a panel_data object, then the name of the panel wave column as a string. Otherwise, leave as NULL, the default.

use.wave

Should the wave be included as a predictor? Default is FALSE.

min.waves

What is the minimum number of waves an individual must have participated in to be included in the analysis? Default is 2 and any valid number is accepted. "all" is also acceptable if you want to include only complete panelists.

variance

One of "toeplitz-1", "constrained", or "unconstrained". The toeplitz variance specification estimates a single error variance and a single lag-1 error correlation with other lags having zero correlation. The constrained model assumes no autocorrelated errors or heteroskedastic errors. The unconstrained option allows separate variances for every period as well as every lag of autocorrelation. This can be very computationally taxing as periods increase and will be inefficient when not necessary. See Allison (2019) for more.

error.type

Either "CR2" or "CR1S". See the clubSandwich package for more details.

...

Ignored.

References

Allison, P. D. (2019). Asymmetric fixed-effects models for panel data. Socius, 5, 1-12. https://doi.org/10.1177/2378023119826441

Examples

if (requireNamespace("clubSandwich")) {
  data("teen_poverty")
  # Convert to long format
  teen <- long_panel(teen_poverty, begin = 1, end = 5)
  model <- fdm(hours ~ lag(pov) + spouse, data = teen)
  summary(model)
}