| Type: | Package |
| Title: | Dynamic Panel Data Models |
| Version: | 0.1.0 |
| Author: | Taha Zaghdoudi |
| Maintainer: | Taha Zaghdoudi <zedtaha@gmail.com> |
| Description: | Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables. |
| License: | GPL-3 |
| LazyData: | TRUE |
| RoxygenNote: | 5.0.1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, gtools |
| NeedsCompilation: | no |
| Packaged: | 2016-08-28 10:51:09 UTC; Asus |
| Repository: | CRAN |
| Date/Publication: | 2016-08-28 13:24:47 |
Dynamic Panel Data Models
Description
This package computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
Details
| Package: | dynpanel |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2016-08-26 |
| License: | GPL-3 |
In this package, we apply the generalized method of moments to estimate the dynamic panel data models.
Author(s)
Taha Zaghdoudi
Taha Zaghdoudi <zedtaha@gmail.com>
References
Anderson, T. W.; Hsiao, Cheng (1981). Estimation of dynamic models with error components. ournal of the American Statistical Association. 76 (375) ,pp. 598-606.
Arellano, Manuel; Bond, Stephen (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies. 58, pp.2)-277. Cameron, A. Colin; Trivedi, Pravin K. (2005). Dynamic Models. Microeconometrics: Methods and Applications. New York: Cambridge University Press. pp. 763-768.
Hsiao, Cheng (2014). Dynamic Simultaneous Equations Models. Analysis of Panel Data. New York: Cambridge University Press. pp. 397-402.
Munnell AH (1990). Why has Productivity Growth Declined? Productivity and Public Investment, New England Economic Review, pp. 3-22.
Examples
# Load data
data(Produc)
# Fit the dynamic panel data using the Arellano Bond (1991) instruments
reg<-dpd(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,Produc,index=c("state","year"),1,4)
summary(reg)
# Fit the dynamic panel data using an automatic selection of appropriate IV matrix
#reg<-dpd(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,Produc,index=c("state","year"),1,0)
#summary(reg)
# Fit the dynamic panel data using the GMM estimator with the smallest set of instruments
#reg<-dpd(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,Produc,index=c("state","year"),1,1)
#summary(reg)
# Fit the dynamic panel data using a reduced form of IV from method 3
#reg<-dpd(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,Produc,index=c("state","year"),1,2)
#summary(reg)
# Fit the dynamic panel data using the IV matrix where the number of moments grows with kT
# K: variables number and T: time per group
#reg<-dpd(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,Produc,index=c("state","year"),1,3)
#summary(reg)
US States Production
Description
statethe state
yearthe year
pcapprivate capital stock
hwyhighway and streets
waterwater and sewer facilities
utilother public buildings and structures
pcpublic capital
gspgross state products
emplabor input measured by the employement in non–agricultural payrolls
unempstate unemployment rate
Usage
data(Produc)
Format
A data frame with 816 rows and 10 variables
method
Description
method
Usage
dpd(x, ...)
Arguments
x |
a numeric design matrix for the model. |
... |
not used |
Author(s)
Zaghdoudi Taha
formula
Description
formula
Usage
## S3 method for class 'formula'
dpd(formula, data = list(), index = c("id", "time"), p,
meth = c(0, 1, 2, 3, 4), ...)
Arguments
formula |
PIB~INF+TIR |
data |
the dataframe |
index |
: id is the name of the identity groups and time is the time per group |
p |
scalar, autoregressive order for dependent variable |
meth |
scalar, indicator for the Instruments to use |
... |
not used |
Summary
Description
Summary
Usage
## S3 method for class 'dpd'
summary(object, ...)
Arguments
object |
is the object of the function |
... |
not used |