这里有几个问题:
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you sometimes use i and sometimes lag_i, but only i is defined. I changed all to lag_i for consistency
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if j = 1 is incorrect syntax. You need if j == 1
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You need to return fit_d so that it persists after your loop
我是通过应用这些变化来完成的
import pandas as pd
import numpy as np
import statsmodels.api as sm
df = pd.DataFrame(np.random.randint(low=0, high=10, size=(100,17)),
columns=['Historic Rate', 'Overnight', '1M', '3M', '6M','1Y','2Y','3Y','4Y','5Y','6Y','7Y','8Y','9Y','10Y','12Y','15Y'])
def Simulation(TotalSim,j):
Super_fit_d = {}
for lag_i in range(1,TotalSim):
#Create a introductory loop to run the first set of regressions
#Each loop produces a univariate regression
#Each loop has a fixed lag of i
fit_d = {} # This will hold all of the fit results and summaries
for col in [x for x in df.columns if x != 'Historic Rate']:
Y = df['Historic Rate'] - df['Historic Rate'].shift(1)
# Need to remove the NaN for fit
Y = Y[Y.notnull()]
X = df[col] - df[col].shift(lag_i)
X = X[X.notnull()]
#Y now has more observations than X due to lag, drop rows to match
Y = Y.drop(Y.index[0:lag_i-1])
if j == 1:
X = sm.add_constant(X) # Add a constant to the fit
fit_d[col] = sm.OLS(Y,X).fit()
#append the dictionary for each lag onto the super dictionary
# return fit_d
Super_fit_d[lag_i] = fit_d
return Super_fit_d
test_dict = Simulation(11,1)
第一滞后
test_dict[1]['Overnight'].summary()
Out[76]:
<class 'statsmodels.iolib.summary.Summary'>
"""
OLS Regression Results
==============================================================================
Dep. Variable: Historic Rate R-squared: 0.042
Model: OLS Adj. R-squared: 0.033
Method: Least Squares F-statistic: 4.303
Date: Fri, 28 Sep 2018 Prob (F-statistic): 0.0407
Time: 11:15:13 Log-Likelihood: -280.39
No. Observations: 99 AIC: 564.8
Df Residuals: 97 BIC: 570.0
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -0.0048 0.417 -0.012 0.991 -0.833 0.823
Overnight 0.2176 0.105 2.074 0.041 0.009 0.426
==============================================================================
Omnibus: 1.449 Durbin-Watson: 2.756
Prob(Omnibus): 0.485 Jarque-Bera (JB): 1.180
Skew: 0.005 Prob(JB): 0.554
Kurtosis: 2.465 Cond. No. 3.98
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
"""
test_dict[2]['Overnight'].summary()
Out[77]:
<class 'statsmodels.iolib.summary.Summary'>
"""
OLS Regression Results
==============================================================================
Dep. Variable: Historic Rate R-squared: 0.001
Model: OLS Adj. R-squared: -0.010
Method: Least Squares F-statistic: 0.06845
Date: Fri, 28 Sep 2018 Prob (F-statistic): 0.794
Time: 11:15:15 Log-Likelihood: -279.44
No. Observations: 98 AIC: 562.9
Df Residuals: 96 BIC: 568.0
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 0.0315 0.428 0.074 0.941 -0.817 0.880
Overnight 0.0291 0.111 0.262 0.794 -0.192 0.250
==============================================================================
Omnibus: 2.457 Durbin-Watson: 2.798
Prob(Omnibus): 0.293 Jarque-Bera (JB): 1.735
Skew: 0.115 Prob(JB): 0.420
Kurtosis: 2.391 Cond. No. 3.84
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
"""
