Economics Analysis

Economics Analysis

Econometrics and economic modeling. Venv: source /Users/zhangmingda/clawd/.venv/bin/activate

Causal Inference Methods

Selection Guide

Difference-in-Differences

import statsmodels.formula.api as smf

# Basic DiD
model = smf.ols('outcome ~ treated * post + C(unit) + C(time)', data=df).fit(cov_type='cluster', cov_kwds={'groups': df['unit']})
print(model.summary())
# DiD estimate = coefficient on treated:post interaction

Instrumental Variables (2SLS)

from linearmodels.iv import IV2SLS

# Y = β₀ + β₁X + ε, where X is endogenous
# Z is the instrument
model = IV2SLS.from_formula('outcome ~ 1 + controls + [endogenous ~ instrument]', data=df)
result = model.fit(cov_type='robust')
print(result.summary)

Regression Discontinuity

# Local linear regression around cutoff
from sklearn.linear_model import LinearRegression

bandwidth = 5  # choose appropriately
cutoff = 0
left = df[(df['running'] >= cutoff - bandwidth) & (df['running'] < cutoff)]
right = df[(df['running'] >= cutoff) & (df['running'] <= cutoff + bandwidth)]

# Fit separate regressions
model_left = LinearRegression().fit(left[['running']], left['outcome'])
model_right = LinearRegression().fit(right[['running']], right['outcome'])

# RDD estimate
rdd_effect = model_right.predict([[cutoff]])[0] - model_left.predict([[cutoff]])[0]

Panel Data

from linearmodels.panel import PanelOLS, RandomEffects, BetweenOLS

df = df.set_index(['entity', 'time'])

# Fixed effects
fe = PanelOLS.from_formula('y ~ x1 + x2 + EntityEffects + TimeEffects', data=df)
fe_result = fe.fit(cov_type='clustered', cluster_entity=True)

# Random effects
re = RandomEffects.from_formula('y ~ x1 + x2', data=df)
re_result = re.fit()

# Hausman test: FE vs RE
# If significant → use FE

Game Theory

import numpy as np
from scipy.optimize import linprog

# Nash equilibrium (2-player, finite)
def find_nash_pure(payoff_A, payoff_B):
    """Find pure strategy Nash equilibria"""
    nash = []
    rows, cols = payoff_A.shape
    for i in range(rows):
        for j in range(cols):
            # Check if i is best response to j, and j is best response to i
            if payoff_A[i,j] == max(payoff_A[:,j]) and payoff_B[i,j] == max(payoff_B[i,:]):
                nash.append((i, j))
    return nash

# Example: Prisoner's Dilemma
A = np.array([[-1, -3], [0, -2]])  # Row player payoffs
B = np.array([[-1, 0], [-3, -2]])  # Column player payoffs
print(f"Nash equilibria: {find_nash_pure(A, B)}")

Economic Data Sources

FRED API

# Get GDP data (need API key)
curl -s "https://api.stlouisfed.org/fred/series/observations?series_id=GDP&api_key=YOUR_KEY&file_type=json"

World Bank API

curl -s "https://api.worldbank.org/v2/country/CHN/indicator/NY.GDP.MKTP.CD?format=json&per_page=20"

Tips

• Always cluster standard errors at the treatment level
• Test parallel trends assumption for DiD
• Report first-stage F-statistic for IV (F > 10 rule of thumb)
• Use robust standard errors by default
• For Chinese economic research, consider CSMAR and CNKI databases
• Report economic significance alongside statistical significance