I use @disaggregate function to distribute quarterly real gdp into monthly one. According to demo, I use option( model=loglin,maintain=average) to distribute log GDP, I also use the option ( model=linear,maintain=sum) to distribute unlogged GDP to verify. However, the two resulting monthly log GDP serires have same fluctuating pattern but different intercept. I consider the method two is correct, how about one? I guess the intercept can not be droped off?
Please Tom give me a help.
Code: Select all
open data us_rqGDP.rat
calendar(m) 1947:01
data(format=rats,compact=average) 1947:01 2019:10 gdp
set ly = log(gdp)
@disaggregate(tsmodel=rwar1,model=loglin,factor=3,$
maintain=average,print) ly / lmy_1
#
graph(head="US monthly distributed log real GDP",overlay=dot,key=upleft,klabels=||"Official","Estimated" ||) 2
# lmy_1
# ly
print / lmy_1 ly
@disaggregate(tsmodel=rwar1,model=linear,factor=3,$
maintain=average,print) gdp / rmgdp2
#
set lmy_2 = log(rmgdp2)
graph(head="US monthly distributed log real GDP",$
key=upleft,overlay=line,klabels=||"Loglin avg","Linear avg"||) 2
# lmy_1 / 1
# lmy_2 / 2
print / ly lmy_1 lmy_2
*** This Model is OK
@disaggregate(tsmodel=rwar1,model=linear,factor=3,$
maintain=sum,print) gdp / rmgdp3
#
set lmy_3 = log(rmgdp3)
print / gdp rmgdp3
graph(head="US monthly distributed log real GDP",$
key=upleft,klabels=||"Loglin avg","Linear avg","Linear sum"||) 3
# lmy_1 / 1
# lmy_2 / 2
# lmy_3 / 3
Hardmann