The RATS Software Forum

Code: Select all

compute temp=mu(3), mu(3)=mu(1),mu(1)=temp

Code: Select all

*
* KIMNP082.RPF
* 4.5 Application 2 from pp 82-86 of Kim and Nelson, "State-space Models
* with Regime Switching"
*
* MS AR model with switches in mean and variance.
*
open data int_cpuq.prn
calendar(q) 1947:1
data(format=free,org=columns) 1947:01 1990:04 qtr tbill cpu
*
set infl = log(cpu/cpu{1})*400.0
*
set rrate = tbill-infl
*
boxjenk(ar=4,constant) rrate 1960:1 *
*
@msvarsetup(lags=2,states=3,switch=mh)
# rrate
*
compute gstart=1960:1,gend=1990:4
frml msvarf = log(%MSVARProb(t))
*
nonlin(parmset=msparms) theta
nonlin(parmset=varparms) mu phi sigmav
@msvarinitial gstart gend
*
* Switch around the mu's to match up with the states in the book.
*
compute temp=mu(3),mu(3)=mu(1),mu(1)=temp
*
* Estimate the model by maximum likelihood. This effectively converges,
* but because some of the theta's are very large negative (producing
* transition  probabilities which are nearly zero), the convergence
* tests fail because the transition probabilities are extremely flat in
* that neighborhood.
*
maximize(parmset=varparms+msparms,$
  start=%(p=%mslogisticp(theta),pstar=%MSVARInit()),$
  reject=%MSVARInitTransition()==0,$
  pmethod=simplex,piters=5,method=bfgs,iters=300) msvarf gstart gend
*
* This pegs the two parameters that are pushed to -infinity as large
* fixed negative values.
*
nonlin(parmset=pegs) theta(1,2)=-50.00 theta(2,3)=-50.00
maximize(parmset=varparms+msparms+pegs,$
  start=%(p=%mslogisticp(theta),pstar=%MSVARInit()),$
  reject=%MSVARInitTransition()==0,$
  method=bfgs,iters=300) msvarf gstart gend

Code: Select all

*
* Switch around the mu's to match up with the states in the book.
*
compute temp=mu(3),mu(3)=mu(1),mu(1)=temp