Introductory-Econometrics

The raw excel data is hard to use.In fact its header file is saved as structureless formal in .des file. Perherps you need to pick it columns manually. So I write a python script to autolink them(.xls file and .des file) So you can analyse it easily.

Function

• load_data(file_name) link two file and return a nice pandas Dataframe object.

• get_data(y_head,x_head,file_name,transform={}) pick the columns you want and drop NA value.the transform can apply log,square etc common transform.

• regress(formula,file_name,summary=True) regress data file by R-style formula.You can study it by statsmodels package document.

• regress_h(y_head,X_head,file_name,summary=True) Unfortunately,R-style formula sometime don't work.You can use it as reserve.

Note: file_name does not contain suffix because it represent two file that are similar name but not same suffix.

Example

Example 8.4 by original book

You only need single line code.

regress('price~lotsize+sqrft+bdrms','hprice1')

output are summary table and result object.

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.672
Model:                            OLS   Adj. R-squared:                  0.661
Method:                 Least Squares   F-statistic:                     57.46
Date:                Sat, 30 Jan 2016   Prob (F-statistic):           2.70e-20
Time:                        10:26:44   Log-Likelihood:                -482.88
No. Observations:                  88   AIC:                             973.8
Df Residuals:                      84   BIC:                             983.7
Df Model:                           3                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept    -21.7703     29.475     -0.739      0.462       -80.385    36.844
lotsize        0.0021      0.001      3.220      0.002         0.001     0.003
sqrft          0.1228      0.013      9.275      0.000         0.096     0.149
bdrms         13.8525      9.010      1.537      0.128        -4.065    31.770
==============================================================================
Omnibus:                       20.398   Durbin-Watson:                   2.110
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               32.278
Skew:                           0.961   Prob(JB):                     9.79e-08
Kurtosis:                       5.261   Cond. No.                     6.41e+04
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.41e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
Out[2]: <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x19ccdb70>