import pandas as pd import numpy as np import matplotlib.pyplot as plt import statsmodels.tsa.stattools as ts data=pd.read_csv('C:/Users/HXWD/Desktop/数据/rb.csv',encoding='gbk') data.columns=['date','open','high','low','close','amt','opi'] data.head() np.log(data['close']).head() x = np.array(np.log(data['close'])) result = ts.adfuller(x, 1,regresults=True) # maxlag is now set to 1 print(result)
#结果：
(-1.0159755159305488, 0.74739309585919544, {'1%': -3.4336805486772994, '10%': -2.5675532292926859, '5%': -2.8630112431183181},
<statsmodels.tsa.stattools.ResultsStore object at 0x0A9C3CD0>)
#以上计算的价格的对数的单位根检验，检验结果不显著，存在单位根。但是计算每天对数收益率的时候，这个是不存在单位根的。
adfuller(x, maxlag=None, regression='c', autolag='AIC', store=False, regresults=False) Augmented Dickey-Fuller unit root test The Augmented Dickey-Fuller test can be used to test for a unit root in a univariate process in the presence of serial correlation. Parameters ---------- x : array_like, 1d data series maxlag : int Maximum lag which is included in test, default 12*(nobs/100)^{1/4} regression : str {'c','ct','ctt','nc'} Constant and trend order to include in regression * 'c' : constant only (default) * 'ct' : constant and trend * 'ctt' : constant, and linear and quadratic trend * 'nc' : no constant, no trend autolag : {'AIC', 'BIC', 't-stat', None} * if None, then maxlag lags are used * if 'AIC' (default) or 'BIC', then the number of lags is chosen to minimize the corresponding information criterium * 't-stat' based choice of maxlag. Starts with maxlag and drops a lag until the t-statistic on the last lag length is significant at the 95 % level. store : bool If True, then a result instance is returned additionally to the adf statistic (default is False) regresults : bool If True, the full regression results are returned (default is False) Returns ------- adf : float Test statistic pvalue : float MacKinnon's approximate p-value based on MacKinnon (1994) usedlag : int Number of lags used. nobs : int Number of observations used for the ADF regression and calculation of the critical values. critical values : dict Critical values for the test statistic at the 1 %, 5 %, and 10 % levels. Based on MacKinnon (2010) icbest : float The maximized information criterion if autolag is not None. regresults : RegressionResults instance The resstore : (optional) instance of ResultStore an instance of a dummy class with results attached as attributes Notes ----- The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the pvalue is above a critical size, then we cannot reject that there is a unit root. The p-values are obtained through regression surface approximation from MacKinnon 1994, but using the updated 2010 tables. If the p-value is close to significant, then the critical values should be used to judge whether to accept or reject the null. The autolag option and maxlag for it are described in Greene. Examples -------- see example script References ---------- Greene Hamilton P-Values (regression surface approximation) MacKinnon, J.G. 1994. "Approximate asymptotic distribution functions for unit-root and cointegration tests. `Journal of Business and Economic Statistics` 12, 167-76. Critical values MacKinnon, J.G. 2010. "Critical Values for Cointegration Tests." Queen's University, Dept of Economics, Working Papers. Available at http://ideas.repec.org/p/qed/wpaper/1227.html
