economics-analysis

Economics Analysis

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

Causal Inference Methods

Selection Guide

Method	When to Use	Key Assumption
RCT	Can randomize treatment	Random assignment
IV (2SLS)	Endogeneity, have instrument	Exclusion restriction
DiD	Policy change, panel data	Parallel trends
RDD	Treatment at threshold	Continuity at cutoff
Matching/PSM	Observational, rich covariates	Selection on observables
Synthetic Control	Aggregate intervention, few treated	Parallel trends (weighted)

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)
```python
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

Source	Data	Access
FRED (St. Louis Fed)	US macro data	https://api.stlouisfed.org/fred/
World Bank	Global development	https://api.worldbank.org/v2/
IMF	International finance	REST API
BLS	US labor statistics	REST API
OECD	OECD country data	REST API
Penn World Table	Cross-country GDP	Download
CNKI/CSMAR	Chinese economic data	Institutional access

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