NH-MSAR-package: (Non) Homogeneous Markov switching autoregressive model In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

NH-MSAR-package is a set of functions to fit, simulate and validate (non) homogeneous Markov Switching Autoregressive models with Gaussian or von Mises innovations.

Details

 Package: NH-MSAR Type: Package Version: 1.0 Date: 2014-08-11 License: What license is it under?

~~ An overview of how to use the package, including the most important ~~ ~~ functions ~~

Author(s)

Val\'e'rie Monbet, [email protected]

References

Hamilton J.D. (1989). A New Approach to the Economic Analysis of Nonstionary Time Series and the Business Cycle. Econometrica 57: 357-384. Ailliot P., Monbet V., (2012), Markov-switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101. Ailliot P., Bessac J., Monbet V., Pene F., (2014) Non-homogeneous hidden Markov-switching models for wind time series. JSPI.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ``` # Fit Homogeneous MS-AR models - univariate time series data(meteo.data) data = array(meteo.data\$temperature,c(31,41,1)) k = 40 T = dim(data)[1] N.samples = dim(data)[2] d = dim(data)[3] M = 2 order = 2 theta.init = init.theta.MSAR(data,M=M,order=order,label="HH") mod.hh = fit.MSAR(data,theta.init,verbose=TRUE,MaxIter=20) regimes.plot.MSAR(mod.hh,data,ylab="temperatures") #Y0 = array(data[1:2,sample(1:dim(data)[2],1),],c(2,1,1)) #Y.sim = simule.nh.MSAR(mod.hh\$theta,Y0 = Y0,T,N.samples = 1) ## Not run # Fit Non Homogeneous MS-AR models - univariate time series #data(lynx) #T = length(lynx) #data = array(log10(lynx),c(T,1,1)) #theta.init = init.theta.MSAR(data,M=2,order=2,label="HH") #mod.lynx.hh = fit.MSAR(data,theta.init,verbose=TRUE,MaxIter=200) #regimes.plot.MSAR(mod.lynx.hh,data,ylab="Captures number") ## End (not run) ```

Example output

```[1] iteration         1                   loglik =        -2483.76821398985
[1] iteration         2                   loglik =        -2481.46191316325
[1] iteration         3                   loglik =        -2480.50800481965
[1] iteration         4                   loglik =        -2478.91074475992
[1] iteration        5                  loglik =       -2476.9747310187
[1] iteration        6                  loglik =       -2475.1342318885
[1] iteration         7                   loglik =        -2473.64252294242
[1] iteration         8                   loglik =        -2472.53158618905
[1] iteration         9                   loglik =        -2471.70564169704
[1] iteration         10                  loglik =        -2471.04140078367
[1] iteration         11                  loglik =        -2470.43217996723
[1] iteration        12                 loglik =       -2469.7793783761
[1] iteration         13                  loglik =        -2468.94324364113
[1] iteration         14                  loglik =        -2467.65737278721
[1] iteration         15                  loglik =        -2465.61710058432
[1] iteration         16                  loglik =        -2463.09808638148
[1] iteration         17                  loglik =        -2460.73052088291
[1] iteration         18                  loglik =        -2458.75581275823
[1] iteration        19                 loglik =       -2457.1641152218
[1] iteration         20                  loglik =        -2455.85659893545
[1] NA NA  2  2  2  2  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  2  2
[26]  2  1  1  1  1  1  1  1
```

NHMSAR documentation built on Dec. 5, 2017, 9:03 a.m.