Hướng dẫn reghdfe python - trăn reghdfe
|
Các ví dụ bao gồm hai phần: mã Python và các bình luận. Mã Python là những ví dụ tối thiểu về hồi quy. Người ta có thể chỉ cần sao chép/dán mã, thay đổi bộ dữ liệu và các tính năng của hồi quy và có tập lệnh hoạt động. Các bình luận bao gồm hai phần: Phần đầu tiên là một hồi quy giống hệt nhau sử dụng gói Reghdfe trong Stata. Phần thứ hai là đầu ra của hồi quy Python tương ứng bằng cách sử dụng regpyhdfe. Những ý kiến đó là có cho mục đích so sánh. Thông tin thời gian là tầm thường và tại thời điểm này không bao gồm - cả Stata và Python chạy ngay lập tức trên CPU máy tính xách tay. Chỉ sử dụng hiệu ứng cố địnhNhững ví dụ này không sử dụng phân cụm. Như bạn có thể thấy, tất cả những gì thực sự cần thiết là một bản dữ liệu gấu trúc. Sau đó chỉ cần truyền trong các đối số theo thứ tự thích hợp (hoặc chỉ cần truyền các đối số được đặt tên. Để biết chi tiết về các tham số, hãy xem tài liệu đối tượng RegpyHDFE) import pandas as pd import numpy as np from regpyhdfe import Regpyhdfe from sklearn.datasets import load_boston def sklearn_to_df(sklearn_dataset): df = pd.DataFrame(sklearn_dataset.data, columns=sklearn_dataset.feature_names) df['target'] = pd.Series(sklearn_dataset.target) return df df = sklearn_to_df(load_boston()) df.to_stata("boston.dta") # . reghdfe target CRIM ZN INDUS NOX AGE, absorb(CHAS RAD) # (MWFE estimator converged in 3 iterations) # # HDFE Linear regression Number of obs = 506 # Absorbing 2 HDFE groups F( 5, 491) = 21.93 # Prob > F = 0.0000 # R-squared = 0.3887 # Adj R-squared = 0.3712 # Within R-sq. = 0.1825 # Root MSE = 7.2929 # # ------------------------------------------------------------------------------ # target | Coef. Std. Err. t P>|t| [95% Conf. Interval] # -------------+---------------------------------------------------------------- # CRIM | -.2089114 .0491012 -4.25 0.000 -.3053857 -.1124371 # ZN | .0679261 .0183051 3.71 0.000 .03196 .1038922 # INDUS | -.2279553 .0860909 -2.65 0.008 -.3971074 -.0588033 # NOX | -9.424849 5.556005 -1.70 0.090 -20.34133 1.49163 # AGE | -.0140739 .0183467 -0.77 0.443 -.0501215 .0219738 # _cons | 31.24755 2.53596 12.32 0.000 26.26487 36.23022 # ------------------------------------------------------------------------------ # # Absorbed degrees of freedom: # -----------------------------------------------------+ # Absorbed FE | Categories - Redundant = Num. Coefs | # -------------+---------------------------------------| # CHAS | 2 0 2 | # RAD | 9 1 8 | # -----------------------------------------------------+ # target~CRIM + ZN + INDUS + NOX + AGE, absorb(CHAS, RAD) # OLS Regression Results # ======================================================================================= # Dep. Variable: target R-squared (uncentered): 0.183 # Model: OLS Adj. R-squared (uncentered): 0.158 # Method: Least Squares F-statistic: 21.93 # Date: Mon, 11 Jan 2021 Prob (F-statistic): 7.57e-20 # Time: 20:30:53 Log-Likelihood: -1715.7 # No. Observations: 506 AIC: 3441. # Df Residuals: 491 BIC: 3463. # Df Model: 5 # Covariance Type: nonrobust # ============================================================================== # coef std err t P>|t| [0.025 0.975] # ------------------------------------------------------------------------------ # CRIM -0.2089 0.049 -4.255 0.000 -0.305 -0.112 # ZN 0.0679 0.018 3.711 0.000 0.032 0.104 # INDUS -0.2280 0.086 -2.648 0.008 -0.397 -0.059 # NOX -9.4248 5.556 -1.696 0.090 -20.341 1.492 # AGE -0.0141 0.018 -0.767 0.443 -0.050 0.022 # ============================================================================== # Omnibus: 172.457 Durbin-Watson: 0.904 # Prob(Omnibus): 0.000 Jarque-Bera (JB): 532.297 # Skew: 1.621 Prob(JB): 2.59e-116 # Kurtosis: 6.839 Cond. No. 480. # ============================================================================== # # Notes: # [1] R² is computed without centering (uncentered) since the model does not contain a constant. # [2] Standard Errors assume that the covariance matrix of the errors is correctly specified. model = Regpyhdfe(df, 'target', ['CRIM', 'ZN', 'INDUS', 'NOX', 'AGE'], ['CHAS', 'RAD']) results = model.fit() print("target~CRIM + ZN + INDUS + NOX + AGE, absorb(CHAS, RAD)") print(results.summary()) import pandas as pd import numpy as np from regpyhdfe import Regpyhdfe # details about dataset can be found at https://www.kaggle.com/crawford/80-cereals df = pd.read_stata('/home/abom/Desktop/regPyHDFE/data/cereal.dta') # . reghdfe rating fat protein carbo sugars, absorb(shelf) # (MWFE estimator converged in 1 iterations) # # HDFE Linear regression Number of obs = 77 # Absorbing 1 HDFE group F( 4, 70) = 54.98 # Prob > F = 0.0000 # R-squared = 0.7862 # Adj R-squared = 0.7679 # Within R-sq. = 0.7586 # Root MSE = 6.7671 # # ------------------------------------------------------------------------------ # rating | Coef. Std. Err. t P>|t| [95% Conf. Interval] # -------------+---------------------------------------------------------------- # fat | -5.684196 .8801468 -6.46 0.000 -7.439594 -3.928799 # protein | 3.740386 .8430319 4.44 0.000 2.059012 5.42176 # carbo | -.7892276 .2041684 -3.87 0.000 -1.196429 -.3820266 # sugars | -2.03286 .2179704 -9.33 0.000 -2.467588 -1.598132 # _cons | 64.49503 4.92674 13.09 0.000 54.66896 74.3211 # ------------------------------------------------------------------------------ # # Absorbed degrees of freedom: # -----------------------------------------------------+ # Absorbed FE | Categories - Redundant = Num. Coefs | # -------------+---------------------------------------| # shelf | 3 0 3 | # -----------------------------------------------------+ # rating ~ fat + protein + carbo + sugars, absorb(shelf) # OLS Regression Results # ======================================================================================= # Dep. Variable: rating R-squared (uncentered): 0.759 # Model: OLS Adj. R-squared (uncentered): 0.734 # Method: Least Squares F-statistic: 54.98 # Date: Mon, 11 Jan 2021 Prob (F-statistic): 6.89e-21 # Time: 20:45:37 Log-Likelihood: -252.82 # No. Observations: 77 AIC: 513.6 # Df Residuals: 70 BIC: 523.0 # Df Model: 4 # Covariance Type: nonrobust # coef std err t P>|t| [0.025 0.975] # ------------------------------------------------------------------------------ # fat -5.6842 0.880 -6.458 0.000 -7.440 -3.929 # protein 3.7404 0.843 4.437 0.000 2.059 5.422 # carbo -0.7892 0.204 -3.866 0.000 -1.196 -0.382 # sugars -2.0329 0.218 -9.326 0.000 -2.468 -1.598 # ============================================================================== # Omnibus: 5.613 Durbin-Watson: 1.801 # Prob(Omnibus): 0.060 Jarque-Bera (JB): 7.673 # Skew: 0.179 Prob(JB): 0.0216 # Kurtosis: 4.504 Cond. No. 5.84 # ============================================================================== residualized = Regpyhdfe(df, 'rating', ['fat', 'protein', 'carbo', 'sugars'], ['shelf']) results = residualized.fit() print("rating ~ fat + protein + carbo + sugars, absorb(shelf)") print(results.summary()) import pandas as pd import numpy as np from regpyhdfe import Regpyhdfe # show variable labels #pd.read_stata('/home/abom/Desktop/regPyHDFE/nlswork.dta', iterator=True).variable_labels() # Load data df = pd.read_stata('/home/abom/Desktop/regPyHDFE/data/cleaned_nlswork.dta') df['hours_log'] = np.log(df['hours']) # . reghdfe ln_wage hours_log, absorb(idcode year) # (dropped 884 singleton observations) # (MWFE estimator converged in 8 iterations) # # HDFE Linear regression Number of obs = 12,568 # Absorbing 2 HDFE groups F( 1, 9454) = 0.50 # Prob > F = 0.4792 # R-squared = 0.7314 # Adj R-squared = 0.6430 # Within R-sq. = 0.0001 # Root MSE = 0.2705 # # ------------------------------------------------------------------------------ # ln_wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] # -------------+---------------------------------------------------------------- # hours_log | -.0058555 .0082759 -0.71 0.479 -.022078 .010367 # _cons | 1.736618 .0292873 59.30 0.000 1.679208 1.794027 # ------------------------------------------------------------------------------ # # Absorbed degrees of freedom: # -----------------------------------------------------+ # Absorbed FE | Categories - Redundant = Num. Coefs | # -------------+---------------------------------------| # idcode | 3102 0 3102 | # year | 12 1 11 | # -----------------------------------------------------+ # ln_wage ~ hours_log, absorb(idcode, year) # OLS Regression Results # ======================================================================================= # Dep. Variable: ln_wage R-squared (uncentered): 0.000 # Model: OLS Adj. R-squared (uncentered): -0.329 # Method: Least Squares F-statistic: 0.5006 # Date: Mon, 11 Jan 2021 Prob (F-statistic): 0.479 # Time: 21:07:22 Log-Likelihood: 386.59 # No. Observations: 12568 AIC: -771.2 # Df Residuals: 9454 BIC: -763.7 # Df Model: 1 # Covariance Type: nonrobust # ============================================================================== # coef std err t P>|t| [0.025 0.975] # ------------------------------------------------------------------------------ # hours_log -0.0059 0.008 -0.708 0.479 -0.022 0.010 # ============================================================================== # Omnibus: 1617.122 Durbin-Watson: 2.143 # Prob(Omnibus): 0.000 Jarque-Bera (JB): 16984.817 # Skew: -0.215 Prob(JB): 0.00 # Kurtosis: 8.679 Cond. No. 1.00 # ============================================================================== model = Regpyhdfe(df, "ln_wage", "hours_log", ["idcode", "year"]) results = model.fit() print("ln_wage ~ hours_log, absorb(idcode, year)") print(results.summary()) Phân cụm: ¶Rất giống với hồi quy tiêu chuẩn, chỉ cần thêm tham số Clustering_ids vào danh sách tham số được chuyển cho Regpyhdfe. from regpyhdfe import Regpyhdfe import pandas as pd import numpy as np df = pd.read_stata('data/cleaned_nlswork.dta') df['hours_log'] = np.log(df['hours']) regpyhdfe = Regpyhdfe(df=df, target='ttl_exp', predictors=['wks_ue', 'tenure'], ids=['idcode'], cluster_ids=['year', 'idcode']) # (dropped 884 singleton observations) # (MWFE estimator converged in 1 iterations) # # HDFE Linear regression Number of obs = 12,568 # Absorbing 1 HDFE group F( 2, 11) = 114.58 # Statistics robust to heteroskedasticity Prob > F = 0.0000 # R-squared = 0.6708 # Adj R-squared = 0.5628 # Number of clusters (year) = 12 Within R-sq. = 0.4536 # Number of clusters (idcode) = 3,102 Root MSE = 2.8836 # # (Std. Err. adjusted for 12 clusters in year idcode) # ------------------------------------------------------------------------------ # | Robust # ttl_exp | Coef. Std. Err. t P>|t| [95% Conf. Interval] # -------------+---------------------------------------------------------------- # wks_ue | .0306653 .0155436 1.97 0.074 -.0035459 .0648765 # tenure | .8513953 .0663892 12.82 0.000 .7052737 .9975169 # _cons | 3.784107 .4974451 7.61 0.000 2.689238 4.878976 # ------------------------------------------------------------------------------ # # Absorbed degrees of freedom: # -----------------------------------------------------+ # Absorbed FE | Categories - Redundant = Num. Coefs | # -------------+---------------------------------------| # idcode | 3102 3102 0 *| # -----------------------------------------------------+ # * = FE nested within cluster; treated as redundant for DoF computation # OLS Regression Results # ======================================================================================= # Dep. Variable: ttl_exp R-squared (uncentered): 0.454 # Model: OLS Adj. R-squared (uncentered): -623.342 # Method: Least Squares F-statistic: 114.8 # Date: Mon, 11 Jan 2021 Prob (F-statistic): 4.28e-08 # Time: 21:35:07 Log-Likelihood: -29361. # No. Observations: 12568 AIC: 5.873e+04 # Df Residuals: 11 BIC: 5.874e+04 # Df Model: 2 # Covariance Type: cluster # ============================================================================== # coef std err z P>|z| [0.025 0.975] # ------------------------------------------------------------------------------ # wks_ue 0.0307 0.016 1.975 0.048 0.000 0.061 # tenure 0.8514 0.066 12.831 0.000 0.721 0.981 # ============================================================================== # Omnibus: 2467.595 Durbin-Watson: 1.819 # Prob(Omnibus): 0.000 Jarque-Bera (JB): 8034.980 # Skew: 0.993 Prob(JB): 0.00 # Kurtosis: 6.376 Cond. No. 2.06 # ============================================================================== results = regpyhdfe.fit() print(results.summary()) |
