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I want to reproduce the results of Bianchi et al (2017) Escaping the Great Recession using R and/or Python. Authors in the article used Markov Switching Vector Autoregressive (MSVAR) and Markov Switching Dynamic Stochastic General Equilibrium (MSDSGE) models. I found MSBVAR R package (archived) which can fit MSVAR model. However, yet not able to find any R and/or Python library to implement MSDSGE Model. The out of MSVAR model are not matching with the results of article. I would appreciate it if someone leads me to R and/or Python libraries to reproduce the results of Bianchi et al (2017) Escaping the Great Recession.

library(MSBVAR)
fm1 <- msvar(Y = Data1TS, p = 2, h = 2, niterblkopt = 10)
fm1
fm2 <- msbvar(
   Y              = Data1TS
 , p              = 2
 , h              = 2
 , lambda0        = 0.8
 , lambda1        = 0.15
 , lambda3        = 1
 , lambda4        = 0.25
 , lambda5        = 1
 , mu5            = 0
 , mu6            = 0
 , qm             = 12
 , alpha.prior    = c(100, 30)*diag(2) + matrix(12, 2, 2)
 , prior          = 0
 , max.iter       = 30
 , initialize.opt = NULL
 )

fm2

Here Data1TS is the data for MSVAR model and Data2TS the data for MSDSGE model.

Data

Data1TS <- 
structure(c(-0.00641658440276408, -0.0164288084965151, -0.0210843373493976, 
-0.0382387022016222, -0.0384359400998336, -0.0333504361210877, 
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-0.0218900160170849, -0.0183156760639253, 0.0115667718191378, 
0.0303706245710364, 0.0587434020091946, 0.0462833099579242, 0.0408268733850129, 
0.0132472826086957, -0.00256717439671405, 0.0168367346938775, 
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0.0556, 0.0482, 0.0399, 0.0389, 0.0417, 0.0479, 0.0598, 0.0594, 
0.0592, 0.0657, 0.0833, 0.0898, 0.0894, 0.0857, 0.0788, 0.067, 
0.0557, 0.0386, 0.0456, 0.0547, 0.0475, 0.0354, 0.043, 0.0474, 
0.0514, 0.0654, 0.0782, 0.1056, 0.1, 0.0932, 0.1125, 0.1209, 
0.0935, 0.063, 0.0542, 0.0616, 0.0541, 0.0483, 0.052, 0.0528, 
0.0487, 0.0466, 0.0516, 0.0582, 0.0651, 0.0676, 0.0728, 0.081, 
0.0958, 0.1007, 0.1018, 0.1095, 0.1358, 0.1505, 0.1269, 0.0984, 
0.1585, 0.1657, 0.1778, 0.1758, 0.1359, 0.1423, 0.1451, 0.1101, 
0.0929, 0.0865, 0.088, 0.0946, 0.0943, 0.0969, 0.1056, 0.1139, 
0.0927, 0.0848, 0.0792, 0.079, 0.081, 0.0783, 0.0692, 0.0621, 
0.0627, 0.0622, 0.0665, 0.0684, 0.0692, 0.0666, 0.0716, 0.0798, 
0.0847, 0.0944, 0.0973, 0.0908, 0.0861, 0.0825, 0.0824, 0.0816, 
0.0774, 0.0643, 0.0586, 0.0564, 0.0482, 0.0402, 0.0377, 0.0326, 
0.0304, 0.0304, 0.03, 0.0306, 0.0299, 0.0321, 0.0394, 0.0449, 
0.0517, 0.0581, 0.0602, 0.058, 0.0572, 0.0536, 0.0524, 0.0531, 
0.0528, 0.0528, 0.0552, 0.0553, 0.0551, 0.0552, 0.055, 0.0553, 
0.0486, 0.0473, 0.0475, 0.0509, 0.0531, 0.0568, 0.0627, 0.0652, 
0.0647, 0.0559, 0.0433, 0.035, 0.0213, 0.0173, 0.0175, 0.0174, 
0.0144, 0.0125, 0.0125, 0.0102, 0.01, 0.01, 0.0101, 0.0143, 0.0195, 
0.0247, 0.0294, 0.0346, 0.0398, 0.0446, 0.0491, 0.0525, 0.0525, 
0.0526, 0.0525, 0.0507, 0.045, 0.0318, 0.0209, 0.0194, 0.0051, 
0.0018, 0.0018, 0.0016, 0.0012, 0.0013, 0.0019, 0.0019, 0.0019, 
0.0016, 0.0009, 0.0008, 0.0007, 0.001, 0.0015, 0.0014, 0.0016, 
0.0014, 0.0012, 0.0008, 0.0009), .Dim = c(236L, 4L), .Dimnames = list(
    NULL, c("deficit_debt", "gdp_growth", "inflation", "interest_rate"
    )), .Tsp = c(1955.25, 2014, 4), class = c("mts", "ts", "matrix"
))
$\endgroup$

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