dynpanel-package

Dynamic Panel Data Models

This package computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables. package

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)