# Ordinary Least SquaresΒΆ

In [1]:
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std

np.random.seed(9876789)


## OLS estimation

Artificial data:

In [2]:
nsample = 100
x = np.linspace(0, 10, 100)
X = np.column_stack((x, x**2))
beta = np.array([1, 0.1, 10])
e = np.random.normal(size=nsample)


Our model needs an intercept so we add a column of 1s:

In [3]:
X = sm.add_constant(X)
y = np.dot(X, beta) + e


Fit and summary:

In [4]:
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())

                            OLS Regression Results
==============================================================================
Dep. Variable:                      y   R-squared:                       1.000
Method:                 Least Squares   F-statistic:                 4.020e+06
Date:                Tue, 02 Dec 2014   Prob (F-statistic):          2.83e-239
Time:                        12:53:08   Log-Likelihood:                -146.51
No. Observations:                 100   AIC:                             299.0
Df Residuals:                      97   BIC:                             306.8
Df Model:                           2
Covariance Type:            nonrobust
==============================================================================
coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
const          1.3423      0.313      4.292      0.000         0.722     1.963
x1            -0.0402      0.145     -0.278      0.781        -0.327     0.247
x2            10.0103      0.014    715.745      0.000         9.982    10.038
==============================================================================
Omnibus:                        2.042   Durbin-Watson:                   2.274
Prob(Omnibus):                  0.360   Jarque-Bera (JB):                1.875
Skew:                           0.234   Prob(JB):                        0.392
Kurtosis:                       2.519   Cond. No.                         144.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



Quantities of interest can be extracted directly from the fitted model. Type dir(results) for a full list. Here are some examples:

In [5]:
print('Parameters: ', results.params)
print('R2: ', results.rsquared)

Parameters:  [  1.3423  -0.0402  10.0103]
R2:  0.999987936503



## OLS non-linear curve but linear in parameters

We simulate artificial data with a non-linear relationship between x and y:

In [6]:
nsample = 50
sig = 0.5
x = np.linspace(0, 20, nsample)
X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))
beta = [0.5, 0.5, -0.02, 5.]

y_true = np.dot(X, beta)
y = y_true + sig * np.random.normal(size=nsample)


Fit and summary:

In [7]:
res = sm.OLS(y, X).fit()
print(res.summary())

                            OLS Regression Results
==============================================================================
Dep. Variable:                      y   R-squared:                       0.933
Method:                 Least Squares   F-statistic:                     211.8
Date:                Tue, 02 Dec 2014   Prob (F-statistic):           6.30e-27
Time:                        12:53:09   Log-Likelihood:                -34.438
No. Observations:                  50   AIC:                             76.88
Df Residuals:                      46   BIC:                             84.52
Df Model:                           3
Covariance Type:            nonrobust
==============================================================================
coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1             0.4687      0.026     17.751      0.000         0.416     0.522
x2             0.4836      0.104      4.659      0.000         0.275     0.693
x3            -0.0174      0.002     -7.507      0.000        -0.022    -0.013
const          5.2058      0.171     30.405      0.000         4.861     5.550
==============================================================================
Omnibus:                        0.655   Durbin-Watson:                   2.896
Prob(Omnibus):                  0.721   Jarque-Bera (JB):                0.360
Skew:                           0.207   Prob(JB):                        0.835
Kurtosis:                       3.026   Cond. No.                         221.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



Extract other quantities of interest:

In [8]:
print('Parameters: ', res.params)
print('Standard errors: ', res.bse)
print('Predicted values: ', res.predict())

Parameters:  [ 0.4687  0.4836 -0.0174  5.2058]
Standard errors:  [ 0.0264  0.1038  0.0023  0.1712]
Predicted values:  [  4.7707   5.2221   5.6362   5.9866   6.2564   6.4412   6.5493   6.6009
6.6243   6.6518   6.7138   6.8341   7.0262   7.2905   7.6149   7.9763
8.3446   8.6876   8.9764   9.19     9.3187   9.3659   9.3474   9.2889
9.2217   9.1775   9.1834   9.2571   9.4044   9.6181   9.879   10.1591
10.4266  10.6505  10.8063  10.8795  10.8683  10.7838  10.6483  10.4913
10.3452  10.2393  10.1957  10.2249  10.3249  10.4808  10.6678  10.8549
11.0101  11.1058]



Draw a plot to compare the true relationship to OLS predictions. Confidence intervals around the predictions are built using the wls_prediction_std command.

In [9]:
prstd, iv_l, iv_u = wls_prediction_std(res)

fig, ax = plt.subplots(figsize=(8,6))

ax.plot(x, y, 'o', label="data")
ax.plot(x, y_true, 'b-', label="True")
ax.plot(x, res.fittedvalues, 'r--.', label="OLS")
ax.plot(x, iv_u, 'r--')
ax.plot(x, iv_l, 'r--')
ax.legend(loc='best');


## OLS with dummy variables

We generate some artificial data. There are 3 groups which will be modelled using dummy variables. Group 0 is the omitted/benchmark category.

In [10]:
nsample = 50
groups = np.zeros(nsample, int)
groups[20:40] = 1
groups[40:] = 2
#dummy = (groups[:,None] == np.unique(groups)).astype(float)

dummy = sm.categorical(groups, drop=True)
x = np.linspace(0, 20, nsample)
# drop reference category
X = np.column_stack((x, dummy[:,1:]))

beta = [1., 3, -3, 10]
y_true = np.dot(X, beta)
e = np.random.normal(size=nsample)
y = y_true + e


Inspect the data:

In [11]:
print(X[:5,:])
print(y[:5])
print(groups)
print(dummy[:5,:])

[[ 0.      0.      0.      1.    ]
[ 0.4082  0.      0.      1.    ]
[ 0.8163  0.      0.      1.    ]
[ 1.2245  0.      0.      1.    ]
[ 1.6327  0.      0.      1.    ]]
[  9.2822  10.5048  11.8439  10.3851  12.3794]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 2 2 2 2 2 2 2 2 2 2]
[[ 1.  0.  0.]
[ 1.  0.  0.]
[ 1.  0.  0.]
[ 1.  0.  0.]
[ 1.  0.  0.]]



Fit and summary:

In [12]:
res2 = sm.OLS(y, X).fit()
print(res.summary())

                            OLS Regression Results
==============================================================================
Dep. Variable:                      y   R-squared:                       0.933
Method:                 Least Squares   F-statistic:                     211.8
Date:                Tue, 02 Dec 2014   Prob (F-statistic):           6.30e-27
Time:                        12:53:10   Log-Likelihood:                -34.438
No. Observations:                  50   AIC:                             76.88
Df Residuals:                      46   BIC:                             84.52
Df Model:                           3
Covariance Type:            nonrobust
==============================================================================
coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1             0.4687      0.026     17.751      0.000         0.416     0.522
x2             0.4836      0.104      4.659      0.000         0.275     0.693
x3            -0.0174      0.002     -7.507      0.000        -0.022    -0.013
const          5.2058      0.171     30.405      0.000         4.861     5.550
==============================================================================
Omnibus:                        0.655   Durbin-Watson:                   2.896
Prob(Omnibus):                  0.721   Jarque-Bera (JB):                0.360
Skew:                           0.207   Prob(JB):                        0.835
Kurtosis:                       3.026   Cond. No.                         221.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



Draw a plot to compare the true relationship to OLS predictions:

In [13]:
prstd, iv_l, iv_u = wls_prediction_std(res2)

fig, ax = plt.subplots(figsize=(8,6))

ax.plot(x, y, 'o', label="Data")
ax.plot(x, y_true, 'b-', label="True")
ax.plot(x, res2.fittedvalues, 'r--.', label="Predicted")
ax.plot(x, iv_u, 'r--')
ax.plot(x, iv_l, 'r--')
legend = ax.legend(loc="best")


## Joint hypothesis test

### F test

We want to test the hypothesis that both coefficients on the dummy variables are equal to zero, that is, $R \times \beta = 0$. An F test leads us to strongly reject the null hypothesis of identical constant in the 3 groups:

In [14]:
R = [[0, 1, 0, 0], [0, 0, 1, 0]]
print(np.array(R))
print(res2.f_test(R))

[[0 1 0 0]
[0 0 1 0]]
<F test: F=array([[ 145.4927]]), p=1.28344196173e-20, df_denom=46, df_num=2>



You can also use formula-like syntax to test hypotheses

In [15]:
print(res2.f_test("x2 = x3 = 0"))

<F test: F=array([[ 145.4927]]), p=1.28344196173e-20, df_denom=46, df_num=2>



### Small group effects

If we generate artificial data with smaller group effects, the T test can no longer reject the Null hypothesis:

In [16]:
beta = [1., 0.3, -0.0, 10]
y_true = np.dot(X, beta)
y = y_true + np.random.normal(size=nsample)

res3 = sm.OLS(y, X).fit()

In [17]:
print(res3.f_test(R))

<F test: F=array([[ 1.2249]]), p=0.303186441063, df_denom=46, df_num=2>


In [18]:
print(res3.f_test("x2 = x3 = 0"))

<F test: F=array([[ 1.2249]]), p=0.303186441063, df_denom=46, df_num=2>



### Multicollinearity

The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification.

In [19]:
from statsmodels.datasets.longley import load_pandas


Fit and summary:

In [20]:
ols_model = sm.OLS(y, X)
ols_results = ols_model.fit()
print(ols_results.summary())

                            OLS Regression Results
==============================================================================
Dep. Variable:                 TOTEMP   R-squared:                       0.995
Method:                 Least Squares   F-statistic:                     330.3
Date:                Tue, 02 Dec 2014   Prob (F-statistic):           4.98e-10
Time:                        12:53:12   Log-Likelihood:                -109.62
No. Observations:                  16   AIC:                             233.2
Df Residuals:                       9   BIC:                             238.6
Df Model:                           6
Covariance Type:            nonrobust
==============================================================================
coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
const      -3.482e+06    8.9e+05     -3.911      0.004      -5.5e+06 -1.47e+06
GNPDEFL       15.0619     84.915      0.177      0.863      -177.029   207.153
GNP           -0.0358      0.033     -1.070      0.313        -0.112     0.040
UNEMP         -2.0202      0.488     -4.136      0.003        -3.125    -0.915
ARMED         -1.0332      0.214     -4.822      0.001        -1.518    -0.549
POP           -0.0511      0.226     -0.226      0.826        -0.563     0.460
YEAR        1829.1515    455.478      4.016      0.003       798.788  2859.515
==============================================================================
Omnibus:                        0.749   Durbin-Watson:                   2.559
Prob(Omnibus):                  0.688   Jarque-Bera (JB):                0.684
Skew:                           0.420   Prob(JB):                        0.710
Kurtosis:                       2.434   Cond. No.                     4.86e+09
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 4.86e+09. This might indicate that there are
strong multicollinearity or other numerical problems.


/home/skipper/.virtualenvs/statsmodels/local/lib/python2.7/site-packages/scipy/stats/stats.py:1205: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
int(n))



#### Condition number

One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length:

In [21]:
norm_x = X.values
for i, name in enumerate(X):
if name == "const":
continue
norm_x[:,i] = X[name]/np.linalg.norm(X[name])
norm_xtx = np.dot(norm_x.T,norm_x)


Then, we take the square root of the ratio of the biggest to the smallest eigen values.

In [22]:
eigs = np.linalg.eigvals(norm_xtx)
condition_number = np.sqrt(eigs.max() / eigs.min())
print(condition_number)

56240.8682691



#### Dropping an observation

Greene also points out that dropping a single observation can have a dramatic effect on the coefficient estimates:

In [23]:
ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()
print("Percentage change %4.2f%%\n"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100]))

Percentage change -13.35%
Percentage change -236.18%
Percentage change -23.69%
Percentage change -3.36%
Percentage change -7.26%
Percentage change -200.46%
Percentage change -13.34%



We can also look at formal statistics for this such as the DFBETAS -- a standardized measure of how much each coefficient changes when that observation is left out.

In [24]:
infl = ols_results.get_influence()


In general we may consider DBETAS in absolute value greater than $2/\sqrt{N}$ to be influential observations

In [25]:
2./len(X)**.5

Out[25]:
0.5

In [26]:
print(infl.summary_frame().filter(regex="dfb"))

    dfb_const  dfb_GNPDEFL   dfb_GNP  dfb_UNEMP  dfb_ARMED   dfb_POP  dfb_YEAR
0   -0.016406    -0.234566 -0.045095  -0.121513  -0.149026  0.211057  0.013388
1   -0.020608    -0.289091  0.124453   0.156964   0.287700 -0.161890  0.025958
2   -0.008382     0.007161 -0.016799   0.009575   0.002227  0.014871  0.008103
3    0.018093     0.907968 -0.500022  -0.495996   0.089996  0.711142 -0.040056
4    1.871260    -0.219351  1.611418   1.561520   1.169337 -1.081513 -1.864186
5   -0.321373    -0.077045 -0.198129  -0.192961  -0.430626  0.079916  0.323275
6    0.315945    -0.241983  0.438146   0.471797  -0.019546 -0.448515 -0.307517
7    0.015816    -0.002742  0.018591   0.005064  -0.031320 -0.015823 -0.015583
8   -0.004019    -0.045687  0.023708   0.018125   0.013683 -0.034770  0.005116
9   -1.018242    -0.282131 -0.412621  -0.663904  -0.715020 -0.229501  1.035723
10   0.030947    -0.024781  0.029480   0.035361   0.034508 -0.014194 -0.030805
11   0.005987    -0.079727  0.030276  -0.008883  -0.006854 -0.010693 -0.005323
12  -0.135883     0.092325 -0.253027  -0.211465   0.094720  0.331351  0.129120
13   0.032736    -0.024249  0.017510   0.033242   0.090655  0.007634 -0.033114
14   0.305868     0.148070  0.001428   0.169314   0.253431  0.342982 -0.318031
15  -0.538323     0.432004 -0.261262  -0.143444  -0.360890 -0.467296  0.552421



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