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

In pdynmc: Moment Condition Based Estimation of Linear Dynamic Panel Data Models

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

Description

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

Usage

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.

Details

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

y_{i,t} = y_{i,t-1} \rho_1 + a_i + \varepsilon_{i,t}

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 \insertCiteHanPhi2010;textualpdynmc.

Value

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

Author(s)

Joachim Schnurbus, Markus Fritsch

References

\insertAllCited

Examples

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

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

pdynmc documentation built on Sept. 12, 2024, 7:42 a.m.