汇总不适用于OLS估算

Question

我对我的statsmodels OLS估计有问题。模型运行时没有任何问题，但是当我试图调用一个摘要以便我可以看到实际结果时，我得到了当a的形状和权重不同时需要指定的轴的类型错误。在

我的代码如下：

from __future__ import print_function, division 
import xlrd as xl
import numpy as np
import scipy as sp
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm

file_loc = "/Users/NiklasLindeke/Python/dataset_3.xlsx"
workbook = xl.open_workbook(file_loc)
sheet = workbook.sheet_by_index(0)
tot = sheet.nrows

data = [[sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows)]

rv1 = []
rv5 = []
rv22 = []
rv1fcast = []
T = []
price = []
time = []
retnor = []

model = []

for i in range(1, tot):        
    t = data[i][0]
    ret = data[i][1]
    ret5 = data[i][2]
    ret22 = data[i][3]
    ret1_1 = data[i][4]
    retn = data[i][5]
    t = xl.xldate_as_tuple(t, 0)
    rv1.append(ret)
    rv5.append(ret5)
    rv22.append(ret22)
    rv1fcast.append(ret1_1)
    retnor.append(retn)
    T.append(t)


df = pd.DataFrame({'RVFCAST':rv1fcast, 'RV1':rv1, 'RV5':rv5, 'RV22':rv22,})
df = df[df.RVFCAST != ""]

Model = smf.ols(formula='RVFCAST ~ RV1 + RV5 + RV22', data = df).fit()
print Model.summary()

换句话说，这是行不通的。在

回调如下：

^{pr2}$

我很抱歉，但我不知道该怎么办。在这之后，我还想用一些Newey-West方法对自相关进行校正，我看到您可以用下面的一行：

mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)

但当我尝试用我的模型运行它时，它会返回错误：

ValueError: operands could not be broadcast together with shapes (256,766) (256,1,256) 

但我意识到statsmodels.公式与get_robustcov函数不兼容，但如果是这样，我如何测试自相关呢？在

但我最紧迫的问题是我不能为我的OLS做一个总结。在

按要求，这是我在df中数据集的前30行。在

print df
             RV1          RV22           RV5      RVFCAST
0     0.01553801    0.01309511    0.01081393  0.008421236
1    0.008881671    0.01301336    0.01134905   0.01553801
2     0.01042178    0.01326669    0.01189979  0.008881671
3    0.009809431    0.01334593    0.01170942   0.01042178
4    0.009418737    0.01358808    0.01152253  0.009809431
5     0.01821364    0.01362502    0.01269661  0.009418737
6     0.01163536    0.01331585    0.01147541   0.01821364
7    0.009469907    0.01329509    0.01172988   0.01163536
8    0.008875018    0.01361841    0.01202432  0.009469907
9     0.01528914    0.01430873    0.01233219  0.008875018
10    0.01210761    0.01412724    0.01238776   0.01528914
11    0.01290773     0.0144439    0.01432174   0.01210761
12    0.01094212    0.01425895    0.01493865   0.01290773
13    0.01041433    0.01430177     0.0156763   0.01094212
14    0.01556703     0.0142857    0.01986616   0.01041433
15     0.0217775    0.01430253    0.01864532   0.01556703
16    0.01599228    0.01390088    0.01579069    0.0217775
17    0.01463037    0.01384096    0.01416622   0.01599228
18    0.03136361    0.01395866    0.01398807   0.01463037
19   0.009462822    0.01295695     0.0106063   0.03136361
20   0.007504367    0.01295204    0.01114677  0.009462822
21   0.007869922    0.01300863    0.01267322  0.007504367
22    0.01373964     0.0129547    0.01314553  0.007869922
23    0.01445476    0.01271198       0.01268   0.01373964
24    0.01216517    0.01249902    0.01202476   0.01445476
25     0.0151366    0.01266783     0.0129083   0.01216517
26    0.01023149    0.01258627     0.0146934    0.0151366
27    0.01141199    0.01284094    0.01490637   0.01023149
28    0.01117856    0.01321258    0.01643881   0.01141199
29    0.01658287    0.01340074    0.01597086   0.01117856

Answer 1

我要感谢用户333800的所有帮助！在

如果有人遇到同样的问题，请参考。在

以下代码：

df = pd.DataFrame({'RVFCAST':rv1fcast, 'RV1':rv1, 'RV5':rv5, 'RV22':rv22,})
df = df[df.RVFCAST != ""]
df = df.astype(float)

Model = smf.ols(formula='RVFCAST ~ RV1 + RV5 + RV22', data = df).fit()
mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)

给了我：

^{pr2}$

我现在可以继续写我的论文了：）

Answer 2

我也有同样的问题，发现原因是输入的数据。我把小数点改成“.”来解决这个问题