The RATS Software Forum

yes i did. that specification result does not show a taylor principle (coef on inflation larger than one) and even shows no significant coef on outputgap. this is the motivation to follow a regime switching model ... but using simply two regime returns a negative coef on output when the taylor princ...

a nested switching model may be the right term. this sort of specifications is employed by Davig and Leeper (2011, European Economic review) to estimate a tyalor rule for the USA.
please see the estimated result in Table 1 in the paper at http://www.nber.org/papers/w15133.pdf?new_window=1

dear Tom
i would like to estimate a MS regression with 4 regimes where the variances of the errors will switch across the 4 regimes. But i would like the coefficients to only switch across 2 regimes ie they will be identical across two regimes. how be be specified?

thanks

dear Tod
did you also experience the error message with the new SRC file VARTVPKSC.correctedMay2013.src?
it says "Subscripts Too Large or Non-Positive"
thanks

Dear KI, thank you. actually i just solve the problem myself now before i see your post. The problem was related to the fact that i did not account for the number of lags when specifying the beginning of the estimation period. The way i did left 38 obs for the pre-sample whereas i explicily also put...

Hi Todd
I have not yet manage to find the cause of the error. But one thing that seems strage is that the presample results obtained directly from the code is different from the one that is derived from SCR file. I guess this may be related to the priors used? any intuition?
thanks
romain

thanks
i check the data in the pre-sample and there seems to be no problem there. here are the files.

Regarding the estimation procedure I originally posted, Del Negro and Primiceri (link below) have recent posted a correction to the ordering of Gibbs sampler steps detailed in the appendix to Primiceri (2005, RESTUD) and used in the original version of the code I posted a few years ago. This correc...

thanks. how would you set up the system with a prior and do ESTIMATE?
thanks

thanks. does this mean that my problem does not have a solution in this framework? I do not see how easily i can use the function ESTIMATE to set up a VAR after the MS parameters have been estimated. could you please further help me on this? i can put the MS parameters in the VAR tools as compute sv...

thanks. i do not yet find the way to solve my problem. In the standard VAR approach i think the weak exogeneity assumption is used alread at the estimation stage as: lags 1 to lags det constant ****************************************************************** declare rect priormat(nvar,nvar) input ...

ok thanbks

i will try. what is bordering me is that at each step of the mcm residuals are drawn...both for each state and the common. no such of things are done for the standard bvar...

thanks.
so do i have to add an estimation of the VAR in addition to using nonlin(parmset=allparms) betasys sigmav p?

how can this be done as the betasys and sigmav are already estimated?
thanks

thanks. However, I was not able to create the RECTANGULAR array of dummy observations with estimate(dummy=dd). I guess the reason is because the model is estimated with nonlin (as nonlin(parmset=allparms) betasys sigmav p) and not with function estimate. As such when i run the code it returns the fo...

suppose x1, x2 and x3 are the 3 series of the var with 3 lag. then i would have the following equations x1=c0+c1*x1(-1)+c2*x1(-2)+c3*x1(-3)+ 0+..........+0 eq(1) x2=d0+d1*x1(-1)+d2*x1(-2)+d3*x1(-3)+d4*x2(-1)+d5*x2(-2)+d6*x2(-3)+d7*x3(-1)+d8*x3(-2)+d9*x3(-3) eq(2) x3=h0+d1*x1(-1)+h2*x1(-2)+h3*x1(-3)+...