fitFSD: Fitting firm size-age distributions
In bda: Binned Data Analysis
fit.FSD R Documentation
Fitting firm size-age distributions
Description
To fit firm size and/or age distributions based on binned data.
Usage
fit.FSD(x,breaks,dist)
Arguments
x
'x' can be a vector or a matrix, or a 'histogram'.
breaks
a matrix with two columns if 'x' is a matrix. Otherwise, it is a vector. Can be missing if 'x' is a vector and 'x' will be grouped uisng the default parameters with hist.
dist
distribution type for 'x' and/or 'y'. Options include Weibull, gpd – generalized Pareto distribution, pd or
Pareto, EWD– exponentiated Weibull distribtion.
Value
x.fit, y.fit
Fitted marginal distribution for row- and column-data when 'x' is a matrix.
Psi
Plackett estimate of the Psi. ONLY for 'fit.FSD2'
If each of x.fit and y.fit, the following values are available:
xhist
histogram.
dist
distribution type. Options include Weibull,
gpd – generalized Pareto distribution, pd or
Pareto, EWD– exponentiated Weibull distribtion.
size
Total number of observations (sample size)
pars
Estimates of parameters.
y, y2, x
the pdf (y) and cdf (y2) values evaluated on a grid x
Examples
x <- rweibull(1000,2,1)
(out <- fit.FSD(x))
data(FSD)
b <- c(0,1:5,6,11,16,21,26,38,Inf);
x <- as.numeric(FirmAge[38,]);
## not run
##(out <- fit.FSD(x))#treated as raw data
xh <- binning(counts=x, breaks=b)
(out <- fit.FSD(xh))
#(out <- fit.FSD(xh,dist="ewd"))
(out <- fit.FSD(xh,dist="pd"))
(out <- fit.FSD(xh,dist="gpd"))
x <- as.numeric(FirmSize[nrow(FirmSize),])
brks.size <- c(0,4.5,9.5,19.5,49.5,99.5,249.5,499.5,
999.5,2499.5,4999.5, 9999.5,Inf)
xh <- binning(counts=x,breaks=brks.size)
Fn <- cumsum(x)/sum(x);Fn
k <- length(Fn)
i <- c((k-5):(k-1))
out1 <- fit.GLD(xh, qtl=brks.size[i+1],
qtl.levels=Fn[i],lbound=0)
i <- c(2,3,4,10,11)
out2 <- fit.GLD(xh, qtl=brks.size[i+1],
qtl.levels=Fn[i],lbound=0)
i <- c(1,2,8,9,10)
out3 <- fit.GLD(xh, qtl=brks.size[i+1],
qtl.levels=Fn[i],lbound=0)
plot(xh,xlim=c(0,120))
lines(out1, col=2)
lines(out2, col=3)
lines(out3, col=4)
ZipfPlot(xh,plot=TRUE)
lines(log(1-out1$y2)~log(out1$x), col=2)
lines(log(1-out2$y2)~log(out2$x), col=3)
lines(log(1-out3$y2)~log(out3$x), col=4)
## sample codes for the figures and tables in the PLoS ONE manuscript
## Table 1 *****************************************************
#xtable(Firm2)
## Figure 1 ****************************************************
#rm(list=ls())
#require(bda)
#data(FSD)
##postscript(file='fig1.eps',paper='letter')
#par(mfrow=c(2,2))
#tmp <- ImportFSD(FirmAge, year=2014,type="age");
#xh <- tmp$age
#plot(xh, xlab="X", main="(a) Histogram of 2014 firm age data")
#fit0 <- fit.FSD(xh, dist='exp')
#lines(fit0$x.fit$ly~fit0$x.fit$lx, lty=2)
#ZipfPlot(fit0, lty=2,
# xlab="log(b)",ylab="log(r)",
# main="(b) Zipf plot of firm age (2014)")
#tmp <- ImportFSD(FirmAge, year=1988,type="age");
#xh2 <- tmp$age
#fit1 <- fit.FSD(xh2, dist='exp')
#ZipfPlot(fit1, lty=2,
# xlab="log(b)",ylab="log(r)",
# main="(c) Zipf plot of firm age (1988)")
#ZipfPlot(fit0, lty=2, type='l',
# xlab="log(b)",ylab="log(r)",
# main="(d) Zipf plots of firm age (1979-2014)")
#for(year in 1979:2014){
# tmp <- ImportFSD(FirmAge, year=year,type="age")
# tmp2 <- ZipfPlot(tmp$age, plot=FALSE)
# lines(tmp2)
#}
#dev.off()
## Figure 2 ****************************************************
#rm(list=ls())
#require(bda)
#data(FSD)
#postscript(file='fig2.eps',paper='letter')
#par(mfrow=c(2,2))
## plot (a)
#tmp <- ImportFSD(FirmSize, year=2014,type="size");
#yh <- tmp$size
#plot(yh, xlab="Y", main="(a) Histogram of firm size (2014)")
## plot (b)
#lbrks <- log(yh$breaks); lbrks[1] <- log(1)
#cnts <- yh$freq
#xhist2 <- binning(counts=cnts,breaks=lbrks)
#plot(xhist2,xlab="log(Y)",
# main="(b) Histogram of firm size (2014, log-scale)")
## plot (c)
#ZipfPlot(yh, plot=TRUE,
# main="(c) Zipf plot firm size (2014)",
# xlab="log(r)",ylab="log(b)")
## plot (d)
#ZipfPlot(yh, plot=TRUE,type='l',
# main="(d) Zipf plots of firm size (1977-2014)",
# xlab="log(r)",ylab="log(b)")
#res <- NULL
#for(year in 1977:2014){
# tmp <- ImportFSD(FirmSize,year=year,type="size");
# yh0 <- tmp$size
# zipf1 <- ZipfPlot(yh0,plot.new=FALSE)
# lines(zipf1)
# res <- c(res, zipf1$slope)
#}
#dev.off()
#mean(res); sd(res)
#quantile(res, prob=c(0.025,0.097))
## Figure 3a & 3b ****************************************************
#rm(list=ls())
#require(bda)
#data(FSD)
#postscript(file='fig3a.eps',paper='letter')
tmp <- ImportFSD(FirmAge, year=2014,type="age");
xh <- tmp$age
(fit1 <- fit.FSD(xh, dist='exp'));
(fit2 <- fit.FSD(xh, dist='weibull'));
#(fit3 <- fit.FSD(xh, dist='ewd')); #slow
#(fit0 <- fit.FSD(xh, dist='gpd')); # test, not used
(fit4 <- fit.FSD(xh, dist='gld'));
#plot(xh, xlab="X", main="(a) Fitted Distribtions")
#lines(fit1, lty=1, col=1,lwd=3)
#lines(fit2, lty=2, col=1,lwd=3)
#lines(fit3, lty=3, col=1,lwd=3)
#lines(fit4, lty=4, col=1,lwd=3)
#lines(fit0, lty=4, col=2,lwd=3)
legend("topright",cex=2,
legend=c("EXP","Weibull","EWD","GLD"),
lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
dev.off()
## Table 3
#r2 <- c(fit1$x.fit$Dn.Zipf, fit2$x.fit$Dn.Zipf,
# fit3$x.fit$Dn.Zipf, fit4$x.fit$Dn.Zipf)
#dn <- c(fit1$x.fit$Dn, fit2$x.fit$Dn,
# fit3$x.fit$Dn, fit4$x.fit$Dn)
#aic <- c(fit1$x.fit$AIC, fit2$x.fit$AIC,
# fit3$x.fit$AIC, fit4$x.fit$AIC)
#bic <- c(fit1$x.fit$BIC, fit2$x.fit$BIC,
# fit3$x.fit$BIC, fit4$x.fit$BIC)
#aicc <- c(fit1$x.fit$AICc, fit2$x.fit$AICc,
# fit3$x.fit$AICc, fit4$x.fit$AICc)
#tbl3 <- data.frame(
# Dn.Zipf = r2, Dn=dn, AIC=aic, BIC=bic,AICc=aicc)
#rownames(tbl3) <- c("EXP","WD","EWD","GLD")
##save(tbl3, file='tbl3.Rdata')
#tbl3
#require(xtable)
#xtable(tbl3,digits=c(0,4,4,0,0,0))
#postscript(file='fig3b.eps',paper='letter')
#par(mfrow=c(1,1))
#ZipfPlot(fit1, plot.new=TRUE,col=1,lwd=3,lty=1,
# xlab="log(x)",ylab="log(S(x))",
# main="(b) Zipf Plots of Fitted Distribtions")
#ZipfPlot(fit2, plot.new=FALSE,col=1,lty=2,lwd=3)
#ZipfPlot(fit3, plot.new=FALSE,col=1,lty=3,lwd=3)
#ZipfPlot(fit4, plot.new=FALSE,col=1,lty=4,lwd=3)
#legend("bottomleft",cex=2,
# legend=c("EXP","Weibull","EWD","GLD"),
# lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
#dev.off()
## TABLE 4 ############################################
## More Results **************************************
#mysummary <- function(x,dist){
# .winner <- function(x,DIST=dist){
# isele <- which(x==min(x))
# if(length(isele)>1)
# warning("multiple winners, only the first is used")
# DIST[isele[1]]
# }
# .mysum <- function(x,y) sum(y==x)
# mu1 <- tapply(x$Dn.Zipf,x$Dist, mean, na.rm=TRUE)
# mu2 <- tapply(x$Dn,x$Dist, mean, na.rm=TRUE)
# sd1 <- tapply(x$Dn.Zipf,x$Dist, sd, na.rm=TRUE)
# sd2 <- tapply(x$Dn,x$Dist, sd, na.rm=TRUE)
# win1 <- tapply(x$Dn.Zipf,x$Year, .winner,DIST=dist)
# win2 <- tapply(x$Dn,x$Year, .winner,DIST=dist)
# win3 <- tapply(x$BIC,x$Year, .winner,DIST=dist)
# dist0 <- levels(as.factor(x$Dist))
# out1 <- sapply(dist0, .mysum,y=win1)
# out2 <- sapply(dist0, .mysum,y=win2)
# out3 <- sapply(dist0, .mysum,y=win3)
# #sele1 <- match(names(out1), dist0)
# #sele2 <- match(names(out2), dist0)
# #sele3 <- match(names(out3), dist0)
# out <- data.frame(Mean.Dn.Zipf=mu1, SD.Dn.Zipf=sd1,
# Mean.Dn=mu2, SD.Dn=sd2,
# Win.Zipf=out1,
# Win.Dn=out2,
# Win.BIC=out3)
# out
#}
#require(bda) #version 14.3.11+
#data(FSD)
#Dn.Zipf <- NULL
#Dn <- NULL
#BIC <- NULL
#Year <- NULL
#DIST <- NULL; dist0 <- c("EXP","WD","EWD","GLD")
#for(year in 1983:2014){
# tmp <- ImportFSD(FirmAge, year=year,type="age");
# xh <- tmp$age
# fit1 <- fit.FSD(xh, dist='exp');fit1
# DIST <- c(DIST,"EXP")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='weibull');fit1
# DIST <- c(DIST,"WD")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='ewd');fit1
# DIST <- c(DIST,"EWD")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='gld');fit1
# DIST <- c(DIST,"GLD")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
#}
#RES <- data.frame(Year=Year,Dist=DIST,Dn.Zipf=Dn.Zipf,Dn=Dn,BIC=BIC)
#save(RES, file='tbl4.Rdata')
#load(file='tbl4.Rdata')
#DIST <- c("EXP","WD","EWD","GLD")
#sele <- RES$Year>=1983 & RES$Year<=1987;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=1988 & RES$Year<=1992;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=1993 & RES$Year<=1997;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=1998 & RES$Year<=2002;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=2003 & RES$Year<=2014;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#(out <- mysummary(RES,dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
## Figure 4a & 4b ******************************************
#rm(list=ls())
#require(bda)
#data(FSD)
tmp <- ImportFSD(FirmSize, year=2014,type="size");
yh <- tmp$size
(fit1 <- fit.FSD(yh, dist='lognormal'));
(fit2 <- fit.FSD(yh, dist='pareto'));
(fit3 <- fit.FSD(yh, dist='gpd'));
(fit4 <- fit.FSD(yh, dist='gld'));
#postscript(file='fig4a.eps',paper='letter')
#par(mfrow=c(1,1))
#plot(yh, xlab="Y", main="(a) Fitted Distributions",
# xlim=c(0,50))
#lines(fit1, lty=1, col=1,lwd=3)
#lines(fit2, lty=2, col=1,lwd=3)
#lines(fit3, lty=3, col=1,lwd=3)
#lines(fit4, lty=4, col=1,lwd=3)
#legend("topright",cex=2,
# legend=c("LN","PD","GPD","GLD"),
# lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
#dev.off()
#postscript(file='fig4b.eps',paper='letter')
#par(mfrow=c(1,1))
#ZipfPlot(fit1, plot.new=TRUE,col=1,lwd=3,lty=1,
# xlab="log(y)",ylab="log(S(y))",
# main="(b) Zipf Plots of Fitted Distribtions")
#ZipfPlot(fit2, plot.new=FALSE,col=1,lty=2,lwd=3)
#ZipfPlot(fit3, plot.new=FALSE,col=1,lty=3,lwd=3)
#ZipfPlot(fit4, plot.new=FALSE,col=1,lty=4,lwd=3)
#legend("bottomleft",cex=2,
# legend=c("LN","PD","GPD","GLD"),
# lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
#dev.off()
## TABLE 5 ############################################
## More information about firm size
#Dn.Zipf <- NULL
#Dn <- NULL
#BIC <- NULL
#Year <- NULL
#DIST <- NULL;
#dist0 <- c("LN","PD","GPD","GLD")
#for(year in 1977:2014){
# DIST <- c(DIST,dist0)
# Year <- c(Year, rep(year,length(dist0)))
# tmp <- ImportFSD(FirmSize, year=year,type="size");
# xh <- tmp$size
# fit1 <- fit.FSD(xh, dist='lognormal');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='pd');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='gpd');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='gld');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
#}
#RES <- data.frame(Year=Year,Dist=DIST,Dn.Zipf=Dn.Zipf,Dn=Dn,BIC=BIC)
#save(RES, file='tbl5.Rdata')
#load(file='tbl5.Rdata')
#dist0 <- c("LN","PD","GPD","GLD")
#(out <- mysummary(RES,dist=dist0))
#require(xtable)
#xtable(out[c(3,2,1),],digits=c(0,4,4,4,4,0,0,0))
## Figure 5 ****************************************************
#xy <- tmp <- ImportFSD(Firm2);
#out1 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("EWD","GPD"));out1
#out2 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("EWD","GLD"));out2
#out3 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("GLD","GPD"));out3
#out4 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("GLD","GLD"));out4
#postscript(file="fig5.eps",paper='letter')
#par(mfrow=c(2,2))
#out <- out1
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(a) Contour Plot -- (EWD+GPD)")
#out <- out2
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(b) Contour Plot -- (EWD+GLD)")
#out <- out3
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(c) Contour Plot -- (GLD+GPD)")
#
#out <- out4
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(d) Contour Plot -- (GLD+GLD)")
##dev.off()
## Figure 6 ****************************************************
#xy2 <- tmp <- ImportFSD(Firm2);
## this is an example showing how to use partial data to fit FSD.
## get marginal frequency distribution for firm age:
#(X <- apply(xy2$xy,1,sum));
#brks.age <- c(0,1,2,3,4,5,6,11,16,21,26,38,Inf)
## get marginal frequency distribution for firm size:
#(Y <- apply(xy2$xy,2,sum));
#(Y <- Y[-1])
#brks.size <- c(5,10,20,50,100,250,500,1000,2500,5000,10000,Inf)
#mxy2 <- xy2$xy[,-1]
#(fitx1 <- fit.FSD(X, breaks=brks.age, dist="ewd"))
#(fitx2 <- fit.FSD(X, breaks=brks.age, dist="gld"))
#(fity1 <- fit.FSD(Y, breaks=brks.size, dist="gld"))
#(fity2 <- fit.FSD(Y, breaks=brks.size, dist="pd"))
#(fity3 <- fit.FSD(Y, breaks=brks.size, dist="gpd"))
#(out11 <- fit.Copula(fitx1, fity1, mxy2))
#(out12 <- fit.Copula(fitx1, fity2, mxy2))
#(out21 <- fit.Copula(fitx2, fity1, mxy2))
#(out22 <- fit.Copula(fitx2, fity2, mxy2))
#(out13 <- fit.Copula(fitx1, fity3, mxy2))
#(out23 <- fit.Copula(fitx2, fity3, mxy2))
##postscript(file="fig6.eps",paper='letter')
#par(mfrow=c(1,1))
#plot(out11,grid.size=40,nlevels=20,lty=2,
# ylim=c(4.8,16),xlim=c(0,18),
# xlab="Firm Age",ylab="Firm Size",
# main="(d) Contour Plot -- (GLD+GLD)")
#plot(out12,grid.size=40,nlevels=30, col=2, plot.new=FALSE)
## or use the command below
## plot(out2,grid.size=50,nlevels=50, col=4, add=TRUE)
##dev.off()
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| fit.FSD | R Documentation |
Fitting firm size-age distributions
Description
To fit firm size and/or age distributions based on binned data.
Usage
fit.FSD(x,breaks,dist)
Arguments
x |
'x' can be a vector or a matrix, or a 'histogram'. |
breaks |
a matrix with two columns if 'x' is a matrix. Otherwise, it is a vector. Can be missing if 'x' is a vector and 'x' will be grouped uisng the default parameters with |
dist |
distribution type for 'x' and/or 'y'. Options include |
Value
x.fit, y.fit |
Fitted marginal distribution for row- and column-data when 'x' is a matrix. |
Psi |
Plackett estimate of the Psi. ONLY for 'fit.FSD2' |
If each of x.fit and y.fit, the following values are available:
xhist |
histogram. |
dist |
distribution type. Options include |
size |
Total number of observations (sample size) |
pars |
Estimates of parameters. |
y, y2, x |
the pdf ( |
Examples
x <- rweibull(1000,2,1)
(out <- fit.FSD(x))
data(FSD)
b <- c(0,1:5,6,11,16,21,26,38,Inf);
x <- as.numeric(FirmAge[38,]);
## not run
##(out <- fit.FSD(x))#treated as raw data
xh <- binning(counts=x, breaks=b)
(out <- fit.FSD(xh))
#(out <- fit.FSD(xh,dist="ewd"))
(out <- fit.FSD(xh,dist="pd"))
(out <- fit.FSD(xh,dist="gpd"))
x <- as.numeric(FirmSize[nrow(FirmSize),])
brks.size <- c(0,4.5,9.5,19.5,49.5,99.5,249.5,499.5,
999.5,2499.5,4999.5, 9999.5,Inf)
xh <- binning(counts=x,breaks=brks.size)
Fn <- cumsum(x)/sum(x);Fn
k <- length(Fn)
i <- c((k-5):(k-1))
out1 <- fit.GLD(xh, qtl=brks.size[i+1],
qtl.levels=Fn[i],lbound=0)
i <- c(2,3,4,10,11)
out2 <- fit.GLD(xh, qtl=brks.size[i+1],
qtl.levels=Fn[i],lbound=0)
i <- c(1,2,8,9,10)
out3 <- fit.GLD(xh, qtl=brks.size[i+1],
qtl.levels=Fn[i],lbound=0)
plot(xh,xlim=c(0,120))
lines(out1, col=2)
lines(out2, col=3)
lines(out3, col=4)
ZipfPlot(xh,plot=TRUE)
lines(log(1-out1$y2)~log(out1$x), col=2)
lines(log(1-out2$y2)~log(out2$x), col=3)
lines(log(1-out3$y2)~log(out3$x), col=4)
## sample codes for the figures and tables in the PLoS ONE manuscript
## Table 1 *****************************************************
#xtable(Firm2)
## Figure 1 ****************************************************
#rm(list=ls())
#require(bda)
#data(FSD)
##postscript(file='fig1.eps',paper='letter')
#par(mfrow=c(2,2))
#tmp <- ImportFSD(FirmAge, year=2014,type="age");
#xh <- tmp$age
#plot(xh, xlab="X", main="(a) Histogram of 2014 firm age data")
#fit0 <- fit.FSD(xh, dist='exp')
#lines(fit0$x.fit$ly~fit0$x.fit$lx, lty=2)
#ZipfPlot(fit0, lty=2,
# xlab="log(b)",ylab="log(r)",
# main="(b) Zipf plot of firm age (2014)")
#tmp <- ImportFSD(FirmAge, year=1988,type="age");
#xh2 <- tmp$age
#fit1 <- fit.FSD(xh2, dist='exp')
#ZipfPlot(fit1, lty=2,
# xlab="log(b)",ylab="log(r)",
# main="(c) Zipf plot of firm age (1988)")
#ZipfPlot(fit0, lty=2, type='l',
# xlab="log(b)",ylab="log(r)",
# main="(d) Zipf plots of firm age (1979-2014)")
#for(year in 1979:2014){
# tmp <- ImportFSD(FirmAge, year=year,type="age")
# tmp2 <- ZipfPlot(tmp$age, plot=FALSE)
# lines(tmp2)
#}
#dev.off()
## Figure 2 ****************************************************
#rm(list=ls())
#require(bda)
#data(FSD)
#postscript(file='fig2.eps',paper='letter')
#par(mfrow=c(2,2))
## plot (a)
#tmp <- ImportFSD(FirmSize, year=2014,type="size");
#yh <- tmp$size
#plot(yh, xlab="Y", main="(a) Histogram of firm size (2014)")
## plot (b)
#lbrks <- log(yh$breaks); lbrks[1] <- log(1)
#cnts <- yh$freq
#xhist2 <- binning(counts=cnts,breaks=lbrks)
#plot(xhist2,xlab="log(Y)",
# main="(b) Histogram of firm size (2014, log-scale)")
## plot (c)
#ZipfPlot(yh, plot=TRUE,
# main="(c) Zipf plot firm size (2014)",
# xlab="log(r)",ylab="log(b)")
## plot (d)
#ZipfPlot(yh, plot=TRUE,type='l',
# main="(d) Zipf plots of firm size (1977-2014)",
# xlab="log(r)",ylab="log(b)")
#res <- NULL
#for(year in 1977:2014){
# tmp <- ImportFSD(FirmSize,year=year,type="size");
# yh0 <- tmp$size
# zipf1 <- ZipfPlot(yh0,plot.new=FALSE)
# lines(zipf1)
# res <- c(res, zipf1$slope)
#}
#dev.off()
#mean(res); sd(res)
#quantile(res, prob=c(0.025,0.097))
## Figure 3a & 3b ****************************************************
#rm(list=ls())
#require(bda)
#data(FSD)
#postscript(file='fig3a.eps',paper='letter')
tmp <- ImportFSD(FirmAge, year=2014,type="age");
xh <- tmp$age
(fit1 <- fit.FSD(xh, dist='exp'));
(fit2 <- fit.FSD(xh, dist='weibull'));
#(fit3 <- fit.FSD(xh, dist='ewd')); #slow
#(fit0 <- fit.FSD(xh, dist='gpd')); # test, not used
(fit4 <- fit.FSD(xh, dist='gld'));
#plot(xh, xlab="X", main="(a) Fitted Distribtions")
#lines(fit1, lty=1, col=1,lwd=3)
#lines(fit2, lty=2, col=1,lwd=3)
#lines(fit3, lty=3, col=1,lwd=3)
#lines(fit4, lty=4, col=1,lwd=3)
#lines(fit0, lty=4, col=2,lwd=3)
legend("topright",cex=2,
legend=c("EXP","Weibull","EWD","GLD"),
lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
dev.off()
## Table 3
#r2 <- c(fit1$x.fit$Dn.Zipf, fit2$x.fit$Dn.Zipf,
# fit3$x.fit$Dn.Zipf, fit4$x.fit$Dn.Zipf)
#dn <- c(fit1$x.fit$Dn, fit2$x.fit$Dn,
# fit3$x.fit$Dn, fit4$x.fit$Dn)
#aic <- c(fit1$x.fit$AIC, fit2$x.fit$AIC,
# fit3$x.fit$AIC, fit4$x.fit$AIC)
#bic <- c(fit1$x.fit$BIC, fit2$x.fit$BIC,
# fit3$x.fit$BIC, fit4$x.fit$BIC)
#aicc <- c(fit1$x.fit$AICc, fit2$x.fit$AICc,
# fit3$x.fit$AICc, fit4$x.fit$AICc)
#tbl3 <- data.frame(
# Dn.Zipf = r2, Dn=dn, AIC=aic, BIC=bic,AICc=aicc)
#rownames(tbl3) <- c("EXP","WD","EWD","GLD")
##save(tbl3, file='tbl3.Rdata')
#tbl3
#require(xtable)
#xtable(tbl3,digits=c(0,4,4,0,0,0))
#postscript(file='fig3b.eps',paper='letter')
#par(mfrow=c(1,1))
#ZipfPlot(fit1, plot.new=TRUE,col=1,lwd=3,lty=1,
# xlab="log(x)",ylab="log(S(x))",
# main="(b) Zipf Plots of Fitted Distribtions")
#ZipfPlot(fit2, plot.new=FALSE,col=1,lty=2,lwd=3)
#ZipfPlot(fit3, plot.new=FALSE,col=1,lty=3,lwd=3)
#ZipfPlot(fit4, plot.new=FALSE,col=1,lty=4,lwd=3)
#legend("bottomleft",cex=2,
# legend=c("EXP","Weibull","EWD","GLD"),
# lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
#dev.off()
## TABLE 4 ############################################
## More Results **************************************
#mysummary <- function(x,dist){
# .winner <- function(x,DIST=dist){
# isele <- which(x==min(x))
# if(length(isele)>1)
# warning("multiple winners, only the first is used")
# DIST[isele[1]]
# }
# .mysum <- function(x,y) sum(y==x)
# mu1 <- tapply(x$Dn.Zipf,x$Dist, mean, na.rm=TRUE)
# mu2 <- tapply(x$Dn,x$Dist, mean, na.rm=TRUE)
# sd1 <- tapply(x$Dn.Zipf,x$Dist, sd, na.rm=TRUE)
# sd2 <- tapply(x$Dn,x$Dist, sd, na.rm=TRUE)
# win1 <- tapply(x$Dn.Zipf,x$Year, .winner,DIST=dist)
# win2 <- tapply(x$Dn,x$Year, .winner,DIST=dist)
# win3 <- tapply(x$BIC,x$Year, .winner,DIST=dist)
# dist0 <- levels(as.factor(x$Dist))
# out1 <- sapply(dist0, .mysum,y=win1)
# out2 <- sapply(dist0, .mysum,y=win2)
# out3 <- sapply(dist0, .mysum,y=win3)
# #sele1 <- match(names(out1), dist0)
# #sele2 <- match(names(out2), dist0)
# #sele3 <- match(names(out3), dist0)
# out <- data.frame(Mean.Dn.Zipf=mu1, SD.Dn.Zipf=sd1,
# Mean.Dn=mu2, SD.Dn=sd2,
# Win.Zipf=out1,
# Win.Dn=out2,
# Win.BIC=out3)
# out
#}
#require(bda) #version 14.3.11+
#data(FSD)
#Dn.Zipf <- NULL
#Dn <- NULL
#BIC <- NULL
#Year <- NULL
#DIST <- NULL; dist0 <- c("EXP","WD","EWD","GLD")
#for(year in 1983:2014){
# tmp <- ImportFSD(FirmAge, year=year,type="age");
# xh <- tmp$age
# fit1 <- fit.FSD(xh, dist='exp');fit1
# DIST <- c(DIST,"EXP")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='weibull');fit1
# DIST <- c(DIST,"WD")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='ewd');fit1
# DIST <- c(DIST,"EWD")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='gld');fit1
# DIST <- c(DIST,"GLD")
# Year <- c(Year, year)
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
#}
#RES <- data.frame(Year=Year,Dist=DIST,Dn.Zipf=Dn.Zipf,Dn=Dn,BIC=BIC)
#save(RES, file='tbl4.Rdata')
#load(file='tbl4.Rdata')
#DIST <- c("EXP","WD","EWD","GLD")
#sele <- RES$Year>=1983 & RES$Year<=1987;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=1988 & RES$Year<=1992;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=1993 & RES$Year<=1997;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=1998 & RES$Year<=2002;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#sele <- RES$Year>=2003 & RES$Year<=2014;sum(sele)
#(out <- mysummary(RES[sele,],dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
#(out <- mysummary(RES,dist=DIST))
#xtable(out[c(2,4,1,3),],digits=c(0,4,4,4,4,0,0,0))
## Figure 4a & 4b ******************************************
#rm(list=ls())
#require(bda)
#data(FSD)
tmp <- ImportFSD(FirmSize, year=2014,type="size");
yh <- tmp$size
(fit1 <- fit.FSD(yh, dist='lognormal'));
(fit2 <- fit.FSD(yh, dist='pareto'));
(fit3 <- fit.FSD(yh, dist='gpd'));
(fit4 <- fit.FSD(yh, dist='gld'));
#postscript(file='fig4a.eps',paper='letter')
#par(mfrow=c(1,1))
#plot(yh, xlab="Y", main="(a) Fitted Distributions",
# xlim=c(0,50))
#lines(fit1, lty=1, col=1,lwd=3)
#lines(fit2, lty=2, col=1,lwd=3)
#lines(fit3, lty=3, col=1,lwd=3)
#lines(fit4, lty=4, col=1,lwd=3)
#legend("topright",cex=2,
# legend=c("LN","PD","GPD","GLD"),
# lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
#dev.off()
#postscript(file='fig4b.eps',paper='letter')
#par(mfrow=c(1,1))
#ZipfPlot(fit1, plot.new=TRUE,col=1,lwd=3,lty=1,
# xlab="log(y)",ylab="log(S(y))",
# main="(b) Zipf Plots of Fitted Distribtions")
#ZipfPlot(fit2, plot.new=FALSE,col=1,lty=2,lwd=3)
#ZipfPlot(fit3, plot.new=FALSE,col=1,lty=3,lwd=3)
#ZipfPlot(fit4, plot.new=FALSE,col=1,lty=4,lwd=3)
#legend("bottomleft",cex=2,
# legend=c("LN","PD","GPD","GLD"),
# lty=c(1:4),lwd=rep(3,4),col=rep(1,4))
#dev.off()
## TABLE 5 ############################################
## More information about firm size
#Dn.Zipf <- NULL
#Dn <- NULL
#BIC <- NULL
#Year <- NULL
#DIST <- NULL;
#dist0 <- c("LN","PD","GPD","GLD")
#for(year in 1977:2014){
# DIST <- c(DIST,dist0)
# Year <- c(Year, rep(year,length(dist0)))
# tmp <- ImportFSD(FirmSize, year=year,type="size");
# xh <- tmp$size
# fit1 <- fit.FSD(xh, dist='lognormal');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='pd');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='gpd');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
# fit1 <- fit.FSD(xh, dist='gld');
# Dn.Zipf <- c(Dn.Zipf, fit1$x.fit$Dn.Zipf)
# Dn <- c(Dn, fit1$x.fit$Dn)
# BIC <- c(BIC, fit1$x.fit$BIC)
#}
#RES <- data.frame(Year=Year,Dist=DIST,Dn.Zipf=Dn.Zipf,Dn=Dn,BIC=BIC)
#save(RES, file='tbl5.Rdata')
#load(file='tbl5.Rdata')
#dist0 <- c("LN","PD","GPD","GLD")
#(out <- mysummary(RES,dist=dist0))
#require(xtable)
#xtable(out[c(3,2,1),],digits=c(0,4,4,4,4,0,0,0))
## Figure 5 ****************************************************
#xy <- tmp <- ImportFSD(Firm2);
#out1 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("EWD","GPD"));out1
#out2 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("EWD","GLD"));out2
#out3 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("GLD","GPD"));out3
#out4 <- fit.FSD(xy$xy, breaks=xy$breaks,dist=c("GLD","GLD"));out4
#postscript(file="fig5.eps",paper='letter')
#par(mfrow=c(2,2))
#out <- out1
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(a) Contour Plot -- (EWD+GPD)")
#out <- out2
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(b) Contour Plot -- (EWD+GLD)")
#out <- out3
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(c) Contour Plot -- (GLD+GPD)")
#
#out <- out4
#res2 <- plot(out,grid.size=40,nlevels=30,
# ylim=c(0,15),xlim=c(0,30),
# xlab="Firm Age",ylab="Firm Size",
# main="(d) Contour Plot -- (GLD+GLD)")
##dev.off()
## Figure 6 ****************************************************
#xy2 <- tmp <- ImportFSD(Firm2);
## this is an example showing how to use partial data to fit FSD.
## get marginal frequency distribution for firm age:
#(X <- apply(xy2$xy,1,sum));
#brks.age <- c(0,1,2,3,4,5,6,11,16,21,26,38,Inf)
## get marginal frequency distribution for firm size:
#(Y <- apply(xy2$xy,2,sum));
#(Y <- Y[-1])
#brks.size <- c(5,10,20,50,100,250,500,1000,2500,5000,10000,Inf)
#mxy2 <- xy2$xy[,-1]
#(fitx1 <- fit.FSD(X, breaks=brks.age, dist="ewd"))
#(fitx2 <- fit.FSD(X, breaks=brks.age, dist="gld"))
#(fity1 <- fit.FSD(Y, breaks=brks.size, dist="gld"))
#(fity2 <- fit.FSD(Y, breaks=brks.size, dist="pd"))
#(fity3 <- fit.FSD(Y, breaks=brks.size, dist="gpd"))
#(out11 <- fit.Copula(fitx1, fity1, mxy2))
#(out12 <- fit.Copula(fitx1, fity2, mxy2))
#(out21 <- fit.Copula(fitx2, fity1, mxy2))
#(out22 <- fit.Copula(fitx2, fity2, mxy2))
#(out13 <- fit.Copula(fitx1, fity3, mxy2))
#(out23 <- fit.Copula(fitx2, fity3, mxy2))
##postscript(file="fig6.eps",paper='letter')
#par(mfrow=c(1,1))
#plot(out11,grid.size=40,nlevels=20,lty=2,
# ylim=c(4.8,16),xlim=c(0,18),
# xlab="Firm Age",ylab="Firm Size",
# main="(d) Contour Plot -- (GLD+GLD)")
#plot(out12,grid.size=40,nlevels=30, col=2, plot.new=FALSE)
## or use the command below
## plot(out2,grid.size=50,nlevels=50, col=4, add=TRUE)
##dev.off()
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