Python statsmodel VARMAX Results

Question

Everytime I run a VARMAX model I get different coefficients. Is there any way I could replicate my previous results without imposing a seed? Thank you

I tried to replicate the VARMA(p,q) example posted on the statsmodels webpage: ( https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_varmax.html ). In order to check the replicability of the results, I just added a loop to reestimate the model and a dataframe (parameters) for saving the results. So this is my code:

%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
dta = sm.datasets.webuse('lutkepohl2', 'https://www.stata-press.com/data/r12/')
dta.index = dta.qtr
endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]


exog = endog['dln_consump']

parameters=pd.DataFrame()
for p in  range(10):
    print(p)
    mod = sm.tsa.VARMAX(endog[['dln_inv', 'dln_inc']], order=(1,1))
    res = mod.fit(maxiter=1000, disp=False)
    print(res.summary())
    param= pd.DataFrame(res.params,columns= ["estimation "+str(p)])
    parameters=pd.concat([parameters, param], axis=1)

print(parameters)    

As you can see, the results change everytime I reestimate the model:

                          estimation 0  estimation 1  estimation 2  \
const.dln_inv                 0.010974      0.010934      0.010934   
const.dln_inc                 0.016554      0.016536      0.016536   
L1.dln_inv.dln_inv           -0.010164     -0.010087     -0.010087   
L1.dln_inc.dln_inv            0.360306      0.362187      0.362187   
L1.dln_inv.dln_inc           -0.032975     -0.033071     -0.033071   
L1.dln_inc.dln_inc            0.230657      0.231421      0.231421   
L1.e(dln_inv).dln_inv        -0.249916     -0.250307     -0.250307   
L1.e(dln_inc).dln_inv         0.125546      0.125581      0.125581   
L1.e(dln_inv).dln_inc         0.088878      0.089001      0.089001   
L1.e(dln_inc).dln_inc        -0.235258     -0.235176     -0.235176   
sqrt.var.dln_inv              0.044926      0.044927      0.044927   
sqrt.cov.dln_inv.dln_inc      0.001670      0.001662      0.001662   
sqrt.var.dln_inc              0.011554      0.011554      0.011554    

Answer 1

Thank you. But I tried to replicate the VARMA(p,q) example posted on the statsmodels webpage: ( https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_varmax.html ). In order to check the replicability of the results, I just added a loop to reestimate the model and a dataframe (parameters) for saving the results. So this is my code:

%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
dta = sm.datasets.webuse('lutkepohl2', 'https://www.stata-press.com/data/r12/')
dta.index = dta.qtr
endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]


exog = endog['dln_consump']

parameters=pd.DataFrame()
for p in  range(10):
    print(p)
    mod = sm.tsa.VARMAX(endog[['dln_inv', 'dln_inc']], order=(1,1))
    res = mod.fit(maxiter=1000, disp=False)
    print(res.summary())
    param= pd.DataFrame(res.params,columns= ["estimation "+str(p)])
    parameters=pd.concat([parameters, param], axis=1)

print(parameters)    

As you can see, the results change everytime I reestimate the model:

                          estimation 0  estimation 1  estimation 2  \
const.dln_inv                 0.010974      0.010934      0.010934   
const.dln_inc                 0.016554      0.016536      0.016536   
L1.dln_inv.dln_inv           -0.010164     -0.010087     -0.010087   
L1.dln_inc.dln_inv            0.360306      0.362187      0.362187   
L1.dln_inv.dln_inc           -0.032975     -0.033071     -0.033071   
L1.dln_inc.dln_inc            0.230657      0.231421      0.231421   
L1.e(dln_inv).dln_inv        -0.249916     -0.250307     -0.250307   
L1.e(dln_inc).dln_inv         0.125546      0.125581      0.125581   
L1.e(dln_inv).dln_inc         0.088878      0.089001      0.089001   
L1.e(dln_inc).dln_inc        -0.235258     -0.235176     -0.235176   
sqrt.var.dln_inv              0.044926      0.044927      0.044927   
sqrt.cov.dln_inv.dln_inc      0.001670      0.001662      0.001662   
sqrt.var.dln_inc              0.011554      0.011554      0.011554