economics-analysis

name economics-analysis description Economic analysis including econometrics, causal inference, time series economics, game theory, welfare analysis, and economic modeling. Use when user works with economic data, regression analysis, instrumental variables, difference-in-differences, RDD, panel data, or economic theory. Triggers on "econometrics", "regression", "causal inference", "instrumental variable", "difference-in-differences", "panel data", "game theory", "supply demand", "GDP", "inflation", "economic model". 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) 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