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• 概解statsmodels

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  To fit most of the models covered by statsmodels, you will need to create two design matrices.

  The first is a matrix of endogenous variable(s) (i.e. dependent, response, regressand, etc.). 对应Y

  The second is a matrix of exogenous variable(s) (i.e. independent, predictor, regressor, etc.). 对应X

  class statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs)

• Model fit and summary

  Fitting a model in statsmodels typically involves 3 easy steps:

  1. Use the model class to describe the model
  2. Fit the model using a class method
  3. Inspect the results using a summary method

  mod = sm.OLS(y, X)  # Describe modelres = mod.fit()		# Fit modelres.summary()		# Summarize model

• OLS

• alpha、beta

  x = pd.series # 基准收益X = add_constant(x) # 2-d array y = pd.series # portfolio 收益res = sm.OLS(y, X).fit()alpha, beta = res.params()

  Alpha may be positive or negative and is the result of active investing.

  Beta, on the other hand, can be earned through passive index investing.

• R-squared||coefficient of determination

  ，

  R-squared is also called the coefficient of determination. It’s a statistical measure of how well the regression line fits the data.

  $R^2$ is a statistical measure that represents the proportion of the variance for a dependent variable that’s explained by an independent variable or variables in a regression model.

  用于判定统计模型的解释力。通过度量因变量的variance中可以由自变量解释部分所占的比例。

• Reference

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