econometrics-julia
Julia-based econometric and structural estimation for computationally intensive tasks. Use for structural models, maximum likelihood, GMM, numerical optimization, simulations, and high-performance computing. Covers DataFrames.jl, FixedEffectModels.jl, Optim.jl, and performance optimization.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econometrics-julia --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Julia Econometrics & Structural Estimation ## Core Packages ```julia using DataFrames, CSV # Data handling using FixedEffectModels # Panel regressions using Optim # Optimization using Distributions # Probability distributions using LinearAlgebra # Matrix operations using ForwardDiff # Automatic differentiation using Statistics, StatsBase ``` ## Data Handling (DataFrames.jl) ```julia # Read data df = CSV.read("data/raw/file.csv", DataFrame) # Basic operations transform!(df, :x => (x -> x .* 100) => :x_scaled) subset!(df, :year => y -> y .>= 2000) # Groupby operations gdf = groupby(df, :id) combine(gdf, :y => mean => :mean_y, nrow => :n) # Efficient joins leftjoin!(df, other_df, on = [:id, :year]) ``` ## Panel Regressions (FixedEffectModels.jl) ```julia # Two-way fixed effects est = reg(df, @formula(y ~ treatment + fe(id) + fe(year)), Vcov.cluster(:state)) # IV estimation est_iv = reg(df, @formula(y ~ (endog ~ instrument) + fe(id) + fe(year))) # Extract results coef(est) # Coefficients vcov(est) # Variance-covariance matrix nobs(est) # Observations r2(est) # R-squared ``` ## Performance Optimization ### Memory Efficiency ```julia # Use views instead of copies @views y_subset = df.y
- Core Packages
- Data Handling (DataFrames.jl)
- Panel Regressions (FixedEffectModels.jl)
- Performance Optimization
- Memory Efficiency
- Broadcasting and Vectorization
- Type Stability
- Structural Estimation
- Maximum Likelihood
- Generalized Method of Moments (GMM)
- Simulation-Based Estimation (MSM/Indirect Inference)
- Numerical Methods
- Root Finding (for equilibrium models)
- Integration (for expected values)
What does the econometrics-julia skill do?
Julia-based econometric and structural estimation for computationally intensive tasks. Use for structural models, maximum likelihood, GMM, numerical optimization, simulations, and high-performance computing. Covers DataFrames.jl, FixedEffectModels.jl, Optim.jl, and performance optimization.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econometrics-julia --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.