TVTP Markov Regime switching model

[superper2008]

Hello Tom:

Now I have got all the results by means of the code you help me figure out. Thanks again. However, when I am analyzing the model, I still need to know which state of two states in my model is recession or expansion period. In the code, you use p(1.1) and p(1.2), I am not sure if I can modify into p(1.1) and p(2,2) to identify which state is recession or expansion period. If I only get the result shown below, I can't determine the recession or expansion according to the signs of V1(1), V2(1) and V1(2), V2(2). If I can write the code P(1,1) and P(2.2), then I can identify the recession or expansion period according to the signs of them.

Appreciate you very much for your help.

Sincerely,
Feiyu

MAXIMIZE - Estimation by BHHH
Convergence in 29 Iterations. Final criterion was 0.0000081 <= 0.0000100
Usable Observations 1493
Function Value 4818.4560

Variable Coeff Std Error T-Stat Signif
************************************************************************************
1. V1(1) 33.09651919 7.41884567 4.46114 0.00000815
2. V1(2) -20.10919941 5.01057196 -4.01335 0.00005986
3. V2(1) 9.48357495 4.25329673 2.22970 0.02576738
4. V2(2) -4.71040158 2.78992367 -1.68836 0.09134173
5. BETAS(1)(1) 0.00022131 0.00024208 0.91420 0.36061336
6. BETAS(1)(2) 0.96252328 0.00499307 192.77178 0.00000000
7. BETAS(1)(3) -0.08981680 0.02266712 -3.96243 0.00007419
8. BETAS(2)(1) 0.00146803 0.00175547 0.83626 0.40301048
9. BETAS(2)(2) 0.98587491 0.01025490 96.13696 0.00000000
10. BETAS(2)(3) -0.04525605 0.02520002 -1.79587 0.07251454
11. SIGSQV(1) 0.00006727 0.00000265 25.37280 0.00000000
12. SIGSQV(2) 0.00041067 0.00004017 10.22425 0.00000000

[superper2008]

Hello Tom:

I have solved the problem. I made one mistake previously.

Feiyu

[TomDoan]

In more complicated models, you may not be able to define the regimes with simple labels like "recession" and "expansion". In the case of Hamilton's original model, the only thing that switched was the process mean for the growth rate---no matter what, one would be bigger than the other, so you could distinguish higher growth from lower growth. Instead, you have a 3 coefficient linear regression and variances which switch, so there is not necessarily any simple ordering for the two. If you're fortunate, you'll get readily identifiable regimes, but you can't count on it. (Krolzig's VAR's often have two or more local modes which are quite different in properties, but have almost identical log likelihoods).