Python 3.x 如何在Python中适应受限的VAR模型（statsmodels）？

问题是我对限制VAR模型中的非重要参数感兴趣，比如VAR（2）。在python 3.0x中如何执行此操作

这个问题已经针对R提出了，但不是针对python

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你能帮我弄清楚这一点吗？

据我所知，我找不到一个能够进行这种限制的python包。然而，R中有一个称为vars的包。因此，我必须使用rpy2接口在python中扩充R包

这可能很容易做到，但是有一个应该发生的事件列表，允许您从python调用R函数（甚至是包）作为vars包

使用不同软件包的摘要可在本文档中找到

然而，为了便于说明，这里是我的代码

首先，您必须将变量作为

# Import vars
Rvars = importr("vars", lib_loc = "C:/Users/Rami Chehab/Documents/R/win-library/3.3")

然后需要将VAR模型与您的数据（或数据框架）相匹配，比如说

data

as

t=Rvars.VAR(data,p=2, type='const')

然后，需要在restrict已知的vars包中应用一个不同的函数，该函数删除不重要的参数

# Let us try restricting it
t1=Rvars.restrict(t,method = "ser")

允许您观察数据的最后一个步骤是调用R中的内置函数summary as

# Calling built-in functions from R
Rsummary = robjects.r['summary']

现在将结果打印为

print(Rsummary(t1))

这将给

Estimation results for equation Ireland.Real.Bond: 

================================================== 

Ireland.Real.Bond = Ireland.Real.Bond.l1 + Ireland.Real.Equity.l1 + Ireland.Real.Bond.l2 



                       Estimate Std. Error t value Pr(>|t|)   

Ireland.Real.Bond.l1    0.26926    0.11139   2.417  0.01739 * 

Ireland.Real.Equity.l1 -0.21706    0.07618  -2.849  0.00529 **

Ireland.Real.Bond.l2    0.23929    0.09979   2.398  0.01829 * 

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1





Residual standard error: 14.41 on 103 degrees of freedom

Multiple R-Squared: 0.1041, Adjusted R-squared: 0.07799 

F-statistic: 3.989 on 3 and 103 DF,  p-value: 0.009862 





Estimation results for equation Ireland.Real.Equity: 

==================================================== 

Ireland.Real.Equity = Ireland.Real.Bond.l1 + Ireland.Real.Equity.l1 + const 



                       Estimate Std. Error t value Pr(>|t|)    

Ireland.Real.Bond.l1     0.7253     0.1585   4.575 1.33e-05 ***

Ireland.Real.Equity.l1  -0.3112     0.1068  -2.914 0.004380 ** 

const                    7.7494     2.1057   3.680 0.000373 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1





Residual standard error: 20.58 on 103 degrees of freedom

Multiple R-Squared: 0.2462, Adjusted R-squared: 0.2243 

F-statistic: 11.21 on 3 and 103 DF,  p-value: 1.984e-06 

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另一种方法是使用R函数

首先，需要将包导入python

import rpy2.robjects as robjects, pandas as pd, numpy as np
from rpy2.robjects import r
from rpy2.robjects.numpy2ri import numpy2ri
from rpy2.robjects.packages import importr

将重要的包从python导入R

r('library("vars")')

然后，假设在同一个名为

的目录中有数据。Rdata

# Load the data in R through python this will create a variable B
r('load("here.Rdata")')

# Change the name of the variable to y
r('y=B')

# Run a normal VAR model
r("t=VAR(y, p=5, type='const')")

# Restrict it 
r('t1=restrict(t, method = "ser", thresh = 2.0, resmat = NULL)')

# Then find the summary statistics
r('s=summary(t1)')

# Save the output into text file call it myfile
r('capture.output(s, file = "myfile.txt")')


# Open it and print it in python
f = open('myfile.txt', 'r')
file_contents = f.read()
print (file_contents)

产出如下：

VAR Estimation Results:
========================= 
Endogenous variables: y1, y2 
Deterministic variables: const 
Sample size: 103 
Log Likelihood: 83.772 
Roots of the characteristic polynomial:
0.5334 0.3785 0.3785     0     0     0     0     0     0     0
Call:
VAR(y = y, p = 5, type = "const")


Estimation results for equation y1: 
=================================== 
y1 = y1.l1 + y2.l1 + y1.l2 

      Estimate Std. Error t value Pr(>|t|)   
y1.l1  0.26938    0.11306   2.383  0.01908 * 
y2.l1 -0.21767    0.07725  -2.818  0.00583 **
y1.l2  0.24068    0.10116   2.379  0.01925 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Residual standard error: 0.146 on 100 degrees of freedom
Multiple R-Squared: 0.1047, Adjusted R-squared: 0.07786 
F-statistic: 3.899 on 3 and 100 DF,  p-value: 0.01111 


Estimation results for equation y2: 
=================================== 
y2 = y1.l1 + y2.l1 + const 

      Estimate Std. Error t value Pr(>|t|)    
y1.l1  0.73199    0.16065   4.557 1.47e-05 ***
y2.l1 -0.31753    0.10836  -2.930 0.004196 ** 
const  0.08039    0.02165   3.713 0.000338 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Residual standard error: 0.2082 on 100 degrees of freedom
Multiple R-Squared: 0.251,  Adjusted R-squared: 0.2286 
F-statistic: 11.17 on 3 and 100 DF,  p-value: 2.189e-06 



Covariance matrix of residuals:
        y1      y2
y1 0.02243 0.01573
y2 0.01573 0.04711

Correlation matrix of residuals:
       y1     y2
y1 1.0000 0.4838
y2 0.4838 1.0000