15.6) Python: Fixed and Random Effects

6.1) Book Review: Mostly Harmless Econometrics
• 6.1) Book Review: Most...
6.2) Mostly Harmless Econometrics: The Experimental Ideal
• 6.2) Mostly Harmless E...
6.3) Book Review: Econometric Analysis of Cross Section and Panel Data
• 6.3) Book Review: Econ...
6.4) Why Economists created Econometrics methods rather than run Experiments?
• 6.4) Why Economists cr...
6.5) Is Regression a Necessary Tool to Analyze Experimental Data?
• 6.5) Is Regression a N...
6.6) Book Review: A Guide to Econometrics
• 6.6) Book Review: A Gu...
6.7) Book Review: Econometrics
• 6.7) Book Review: Econ...
6.8) Introductory Books for Econometrics
• 6.8) Introductory Book...
6.9) Mathematical Exposition of Why Random Assignment Eliminates Selection Bias
• 6.9) Mathematical Expo...
6.10) Regression Analysis of Experiments
• 6.10) Regression Analy...
6.11) Field Centipedes
• 6.11) Field Centipedes
6.12) Bias Caused by Bad Controls
• 6.12) Bias Caused by B...
6.13) Structural Econometrics vs Experiment
• 6.13) Structural Econo...
6.14) Are Emily and Greg More Employable Than Lakisha and Jamal?
• 6.14) Are Emily and Gr...
6.15) Times Series vs Cross Section vs Panel Data
• 6.15) Times Series vs ...
7.1) Criteria for Estimators: Unbiasedness
• 7.1) Criteria for Esti...
7.2) Criteria for Estimators: Efficiency
• 7.2) Criteria for Esti...
7.3) Criteria for Estimators: Mean Square Error (MSE)
• 7.3) Criteria for Esti...
7.4) Asymptotic Properties of Estimators
• 7.4) Asymptotic Proper...
7.5) Intuition: Maximum Likelihood Estimator
• 7.5) Intuition: Maximu...
7.6) Simple vs Multiple Regression
• 7.6) Simple vs Multipl...
7.7) T-Test vs F-Test: Joint Hypothesis
• 7.7) T-Test vs F-Test:...
8.1) Law of Iterated Expectation
• 8.1) Law of Iterated E...
8.2) Geometric Interpretation of OLS
• 8.2) Geometric Interpr...
8.3) Ordinary Least Squares: Key Assumption
• 8.3) Ordinary Least Sq...
8.4) Conditional Independence Assumption (CIA)
• 8.4) Conditional Indep...
8.5) Unconditional vs Conditional Variance
• 8.5) Unconditional vs ...
8.6) Homoskedastic vs Heteroskedasticity Errors
• 8.6) Homoskedastic vs ...
9.1) Minimize the Residual Sum of Squares (RSS)
• 9.1) Minimize the Resi...
9.2) OLS Matrix Notation
• 9.2) OLS Matrix Notation
9.3) Projection Matrix: Idempotent and Symmetric
• 9.3) Projection Matrix...
9.4) Orthogonal Projection Matrix
• 9.4) Orthogonal Projec...
9.5) Derivation of R-Squared
• 9.5) Derivation of R-S...
9.6) Orthogonal Partitioned Regression
• 9.6) Orthogonal Partit...
10.1) Unbiasedness of OLS
• 10.1) Unbiasedness of OLS
10.2) Consistency of OLS
• 10.2) Consistency of OLS
10.3) OLS: Variance
• 10.3) OLS: Variance
10.4) Weighted Least Squares (WLS)
• 10.4) Weighted Least S...
10.5) Generalized Least Squares (GLS)
• 10.5) Generalized Leas...
11.1) Omitted Variable Bias: Proxy Solution
• 11.1) Omitted Variable...
11.2) Measurement Error in the Dependent Variable
• 11.2) Measurement Erro...
11.3) Measurement Error in an Explanatory Variable
• 11.3) Measurement Erro...
11.4) Classical Errors-in-Variables and Attenuation Bias
• 11.4) Classical Errors...
12.1) Instrumental Variables (IV): Assumptions
• 12.1) Instrumental Var...
12.2) Why Instrumental Variable?
• 12.2) Why Instrumental...
12.3) Two-Stage Least Squares (2SLS)
• 12.3) Two-Stage Least ...
12.4) Python: IV and 2SLS
• 12.4) Python: IV and 2SLS
13.1) Sharp Regression Discontinuity
• 13.1) Sharp Regression...
13.2) Regression Discontinuity in Python
• 13.2) Regression Disco...
13.3) Regression Discontinuity (RD)
• 13.3) Regression Disco...
13.4) Fuzzy Regression Discontinuity (FRD)
• 13.4) Fuzzy Regression...
13.5) Fuzzy vs Sharp RD
• 13.5) Fuzzy vs Sharp RD
13.6) Python Fuzzy RD
• 13.6) Python: Fuzzy RD
14.1) First-Difference Estimator
• 14.1) First-Difference...
14.2) Algebra of Difference-in-Differences (DID)
• 14.2) Algebra of Diffe...
14.3) Python: Diff-in-Diff (DD)
• 14.3) Python: Diff-in-...
14.4) Quasi-Experiment Diff-in-Diff (DID)
• 14.4) Quasi-Experiment...
15.1) Fixed Effects (FE): Time-Demeaned
• 15.1) Fixed Effects (F...
15.2) Random Effects (RE) vs Fixed Effects (FE)
• 15.2) Random Effects (...
15.3) Random Effects (RE) is Generalized Least Squares (GLS)
• 15.3) Random Effects (...
15.4) Covariance Matrix: Random Effects (RE)
• 15.4) Covariance Matri...
15.5) Random Effects as a Weighted Average of OLS and FE
• 15.5) Random Effects a...
15.6) Python: Fixed and Random Effects
• 15.6) Python: Fixed an...

[@user-bq2zh7th4b]

Qué grande! Muchas gracias eres la primera fuente que encuentro que explica claramente como hacer una regresión de panel en python.

[@Erika112296]

Don't reverse order of observation variable and time variable; if we don't have a DID specification, it is likely to be bias

[@causaldeeplearning4738]

Yes, don't reverse the unit of analysis variable with the time variable. I saw Ph.D. students committing this mistake several times. Some had to rewrite their dissertation after the defense because the results were all off.

[@esekhaigbeeronmonsele7614]

Thank you

[@alexandrerosenberg8067]

Maravilha!

[@causaldeeplearning4738]

Valeu Brazuca!!

[@davidcriss9014]

When running FE in python, make sure entity_effects=True.

[@causaldeeplearning4738]

Or you can implement it manually with dummy variables.