FDLS function

First Difference Least Squares (FDLS) Estimator of Han and Phillips (2010).

FDLS computes closed form estimator for lag parameter of linear dynamic panel data model based on first difference least squares (FDLS) estimator.

FDLS(dat, varname.i, varname.t, varname.y)

Arguments

• dat: A dataset.
• varname.i: The name of the cross-section identifier.
• varname.t: The name of the time-series identifier.
• varname.y: A character string denoting the name of the dependent variable in the dataset.

Returns

An object of class numeric that contains the coefficient estimate for the lag parameter according to the two roots of the quadratic equation.

Details

The function estimates a linear dynamic panel data model of the form

where $y_{i,t-1}$ is the lagged dependent variable, $\rho_1$ is the lag parameter, $a_i$ is an unobserved individual specific effect, and $\varepsilon_{i,t}$ is an idiosyncratic remainder component. The model structure accounts for unobserved individual specific heterogeneity and dynamics. Note that more general lag structures and further covariates are beyond the scope of the current implementation in pdynmc.

More details on the FDLS estimator and its properties are provided in \insertCite HanPhi2010;textualpdynmc.

Examples

## Load data
data(cigDemand, package = "pdynmc")
dat <- cigDemand

## Code example
m1 <- FDLS(dat = dat, varname.i = "state", varname.t = "year", varname.y = "packpc")

References

\insertAllCited

Author(s)

Joachim Schnurbus, Markus Fritsch