Visualization#
The viz module provides publication-quality plots for robustness analysis
results.
Co-Movement Plot (Figure 3.9)#
Creates a 3x2 grid showing cross-correlations at leads/lags (-4 to +4) for five variables: unemployment, productivity, price index, real interest rate, and real wage.
from validation.robustness import plot_comovements
plot_comovements(iv_result, output_dir="output/", show=True)
Impulse-Response Function Plot#
Compares baseline AR(2) IRF (dashed) with cross-simulation mean AR(1) IRF (solid).
from validation.robustness import plot_irf
plot_irf(iv_result, show=True)
Sensitivity Co-Movement Comparison#
Shows how co-movement structure changes across parameter values for each experiment.
from validation.robustness import plot_sensitivity_comovements
for exp_result in sa.experiments.values():
plot_sensitivity_comovements(exp_result, show=True)
PA Experiment Plots#
GDP comparison (Figure 3.10): Side-by-side time series of GDP with and without preferential attachment.
from validation.robustness import plot_pa_gdp_comparison, plot_pa_comovements
plot_pa_gdp_comparison(pa, show=True)
plot_pa_comovements(pa, show=True)
Entry Experiment Plots#
GDP growth and bankruptcy rates across tax rate levels.
from validation.robustness import plot_entry_comparison
plot_entry_comparison(entry, show=True)
API Reference#
Visualization for robustness analysis.
Generates co-movement plots (Figure 3.9 from the book), impulse-response function comparisons, sensitivity analysis summary plots, and structural experiment visualizations (Section 3.10.2).
- validation.robustness.viz.plot_comovements(result, output_dir=None, show=True)[source]#
Plot Figure 3.9: co-movements at leads and lags.
Creates a 3x2 grid (5 panels + 1 empty) showing cross-correlations between HP-filtered GDP and five macroeconomic variables at lags -4 to +4. Baseline run shown as ‘+’, cross-simulation mean as ‘o’.
- validation.robustness.viz.plot_irf(result, output_dir=None, show=True)[source]#
Plot impulse-response function comparison.
Shows the baseline AR(2) IRF and the cross-simulation average AR(1) IRF.
- validation.robustness.viz.plot_sensitivity_comovements(exp_result, output_dir=None, show=True)[source]#
Plot co-movement comparison for a sensitivity experiment.
Shows baseline vs extreme parameter values to illustrate how co-movement structure changes with parameter variation.
- validation.robustness.viz.plot_pa_gdp_comparison(pa_result, output_dir=None, show=True, seed=0)[source]#
Plot GDP time series comparison: PA on vs PA off (Figure 3.10).
Runs two quick single-seed simulations to produce overlaid GDP time series showing how volatility drops when PA is disabled.
- validation.robustness.viz.plot_pa_comovements(pa_result, output_dir=None, show=True)[source]#
Plot co-movement comparison: baseline vs PA-off.
3x2 grid with both conditions overlaid. Shows price index and wages shifting to lagging/acyclical when PA is disabled.
- validation.robustness.viz.plot_entry_comparison(entry_result, output_dir=None, show=True)[source]#
Plot entry neutrality results across tax rates.
Shows unemployment, GDP growth volatility, and collapse rate across profit tax rates. Monotonic degradation confirms entry mechanism does not artificially drive recovery.