Help us understand the problem. What is going on with this article?

インパルス応答の確認(個人的メモ)

More than 5 years have passed since last update.

参考資料

参考コード

# coding: utf-8

import pandas as pd
import numpy as np
import statsmodels.api as sm
import pylab
from statsmodels.tsa.base.datetools import dates_from_str
from statsmodels.tsa.vector_ar.var_model import VAR

mdata = sm.datasets.macrodata.load_pandas().data
dates = mdata[['year', 'quarter']].astype(int).astype(str)
quarterly = dates["year"] + "Q" + dates["quarter"]
quarterly = dates_from_str(quarterly)

mdata = mdata[['realgdp','realcons','realinv']]
mdata.index = pd.DatetimeIndex(quarterly)
data = np.log(mdata).diff().dropna() # log difference

# make a VAR model
model = VAR(data)
results = model.fit(2)
print results.summary()
results.plot()
results.plot_acorr() #autocorrelation 

model.select_order(15)
results = model.fit(maxlags=15, ic='aic')

irf = results.irf(10)
irf.plot(orth=True) #Orthogonalization

pylab.show()
Why not register and get more from Qiita?
  1. We will deliver articles that match you
    By following users and tags, you can catch up information on technical fields that you are interested in as a whole
  2. you can read useful information later efficiently
    By "stocking" the articles you like, you can search right away
Comments
No comments
Sign up for free and join this conversation.
If you already have a Qiita account
Why do not you register as a user and use Qiita more conveniently?
You need to log in to use this function. Qiita can be used more conveniently after logging in.
You seem to be reading articles frequently this month. Qiita can be used more conveniently after logging in.
  1. We will deliver articles that match you
    By following users and tags, you can catch up information on technical fields that you are interested in as a whole
  2. you can read useful information later efficiently
    By "stocking" the articles you like, you can search right away
ユーザーは見つかりませんでした