results_iv = reg_iv.fit(cov_type='unadjusted', debiased=True) #第二步,将残差对所有外生变量和工具变量回归 mroz['resid_iv'] = results_iv.resids reg_aux = smf.ols(formula='resid_iv ~ exper + expersq + motheduc + fatheduc', data=mroz) results_aux = reg_aux.fit() #第三步,显著...
'age','woman','consume','wealth']]x=sm.add_constant(x)model_panel=lm.PanelOLS(y,x,entity_effects=True,time_effects=True,drop_absorbed=True)MODEL1=model_panel.fit(cov_type='clustered',cluster_entity=True,cluster_time=True)# FEOLS-Col_2y=Total_merge.PESGx=Total_merge[['PEB...
fit(cov_type="HAC", cov_kwds={'maxlags':6}).summary() res fullSet = ["mrktEW", "cry", "mom", "basmom",'ivol', 'totalVol', "skew", "relbas"] linReg = OLS(df.bvd12*12, df_factors[["Const"] + fullSet], hasconst=True, missing="drop") res = linReg.fit(cov_type="...
AI代码解释 y=fmdata['ret']x1=fmdata[['pb']+indname]x2=fmdata[['mktcap']+indname]x3=fmdata[['mom1']+indname]x4=fmdata[['roe_ttm']+indname]x5=fmdata[['pb','mktcap','mom1','roe_ttm']+indname]res_fm1=FamaMacBeth(y,sm.add_constant(x1)).fit(cov_type='kernel',debiase...
在上面的代码中,我们首先使用add_constant函数为自变量矩阵添加一个常数列。然后,我们使用OLS函数定义模型,并使用fit方法进行拟合。通过设置cov_type参数为'robust',我们指定使用鲁棒标准误差进行估计。 查看回归结果 最后,我们可以使用summary方法来查看回归结果的摘要信息。
results_HAC = reg_HAC.fit(cov_type='HAC', use_T=True, cov_kwds={'maxlags': 1}) # maxlags表示滞后 return results_HC0.summary().tables[1] def get_predict(model, data, cloumns): model = model.fit() new_x = data.loc[data.Sales.notnull(), cloumns].values ...
问Python statsmodel健壮的cov_type='hac-panel‘问题EN如果秉承着能跑就行的态度写shell脚本，是很自在...
然后我们可以定义我们自己的配色方案并绘制散点图，代码如下所示：# Set a 3 KMeans clusteringkmeans = KMeans(n_clusters=3)# Compute cluster centers and predict cluster indicesX_clustered = kmeans.fit_predict(x_9d)# Define our own color mapLABEL_COLOR_MAP = {0: 'r',1: '...
clst.fit(X) predicted_labels=clst.predict(X) ARIs.append(adjusted_rand_score(labels_true,predicted_labels)) ax.plot(nums,ARIs,marker=markers[i],label="covariance_type:%s"%cov_type) ax.set_xlabel("n_components") ax.legend(loc="best") ...