FDLS {pdynmc} | R Documentation |
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 Han and Phillips (2010).
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
Han C, Phillips PCB (2010). “GMM Estimation For Dynamic Panels With Fixed Effects And Strong Instruments At Unity.” Econometric Theory, 26(1), 119–151. doi:10.1017/S026646660909063X.
Examples
## Load data
data(cigDemand, package = "pdynmc")
dat <- cigDemand
## Code example
m1 <- FDLS(dat = dat, varname.i = "state", varname.t = "year", varname.y = "packpc")