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 ```python 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 ```python # 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 ```python 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 ```python 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 ```bash # 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 ```bash 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