我正在尝试构建一个 super 字典，其中包含许多较低级别的库
概念
我的零售银行有过去 12 年的利率，我正在尝试使用不同债券的投资组合来模拟利率。
回归公式
Y_i - Y_i-1 = A + B(X_i - X_i-1) + E
换句话来说，Y_Lag = alpha + beta(X_Lag) + 误差项
数据
Note: Y = Historic Rate
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'])
到目前为止的代码
#Import packages required for the analysis
import pandas as pd
import numpy as np
import statsmodels.api as sm
def Simulation(TotalSim,j):
#super dictionary to hold all iterations of the loop
Super_fit_d = {}
for 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(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: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
Super_fit_d[lag_i] = fit_d
#Check the output for one column
fit_d['Overnight'].summary()
#Check the output for one column in one segment of the super dictionary
Super_fit_d['lag_5'].fit_d['Overnight'].summary()
Simulation(11,1)
问题
我似乎每次循环都会覆盖我的字典，并且我没有正确评估 i 来将迭代索引为 lag_1、lag_2、lag_3 等。我该如何解决这个问题？
提前致谢
最佳答案
这里有几个问题:
有时使用 i，有时使用 lag_i，但仅定义了 i。为了保持一致性，我全部更改为 lag_iif j = 1 语法不正确。你需要 if j == 1您需要返回 fit_d，以便它在循环后持续存在
我通过应用这些更改来完成它
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)
第一次滞后
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.
"""
第二个滞后
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.
"""
关于Python:简单 OLS super 词典，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/52551247/