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Results
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Python Output
In [79]: res=sm.OLS(data1.SpreadIV,data1[['m','m2','m3']],intercept=False).fit()
In [80]: res.params
Out[80]:
m 0.045335
m2 3.529595
m3 -24.934154
dtype: float64
In [81]: res.resid
Out[81]:
0 0.012871
1 -0.026499
2 -0.001893
3 0.002577
4 0.002764
5 0.005981
6 0.005226
7 0.002640
8 -0.001090
9 -0.005577
10 -0.010838
11 -0.012619
12 -0.005462
13 0.000485
14 0.010591
dtype: float64
Armadillo Output
0.0453
3.5296
-24.9342
0.0129
-0.0265
-0.0019
0.0026
0.0028
0.0060
0.0052
0.0026
-0.0011
-0.0056
-0.0108
-0.0126
-0.0055
0.0005
0.0106
Weighted Regression Test :
Python
In [89]: res1=sm.WLS(data1.SpreadIV,data1[['m','m2','m3']],intercept=False,weights=data1.w1).fit()
In [90]: res1.params
Out[90]:
m -0.129716
m2 2.823257
m3 -24.101442
dtype: float64
In [96]: res1.resid
Out[96]:
0 0.006184
1 -0.035894
2 -0.011512
3 -0.006609
4 -0.005398
5 -0.000626
6 0.000651
7 0.000522
8 -0.000371
9 -0.001684
10 -0.003474
11 -0.001522
12 0.009597
13 0.019703
14 0.034138
dtype: float64
C++:
-0.1297
2.8233
-24.1014
0.0062
-0.0359
-0.0115
-0.0066
-0.0054
-0.0006
0.0007
0.0005
-0.0004
-0.0017
-0.0035
-0.0015
0.0096
0.0197
0.0341