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Macroeconomic Policy Simulators & Interactive Browser Labs

puremacro provides a production-grade, dual-delivery quantitative macroeconomic simulation suite designed for research, policy analysis, and interactive teaching:

Python General Equilibrium API (puremacro.models):
TradePolicySimulator: Multi-country, multi-sector Ricardian general equilibrium model based on Caliendo and Parro (2015, Review of Economic Studies). Solves exact hat algebra counterfactuals for bilateral tariff shocks, terms of trade and input-output intermediate cost cascades, with machine-checked goods market clearing ($\max_i |X_i - (Y_i + R_i + D_i)| < 10^{-6}$). Its welfare_decomposition is a descriptive split with a residual component, as detailed below.
MonetaryTransmissionSimulator: Side-by-side comparative simulation of Heterogeneous-Agent New Keynesian (HANK) and Representative-Agent New Keynesian (RANK) economies, featuring the Kaplan, Moll, and Violante (2018, American Economic Review) direct versus indirect transmission decomposition across empirical Marginal Propensity to Consume (MPC) wealth deciles.
Zero-Build Browser-Native Laboratories (curso/site/labs/):
comercio-aranceles.html / .js: Real-time trade war counterfactual laboratory (MEX-USA-CHN) with live bilateral tariff sliders, client-side general equilibrium solver in vanilla JavaScript (< 2 ms solve time), terms-of-trade shifts, and trade diversion visualizers.
politica-monetaria-hank.html / .js: Interactive HANK versus RANK monetary laboratory with live MPC wealth decile ladders and Kaplan-Moll-Violante direct/indirect channel curves.
Designed for offline operation, zero-build deployment, and native execution on tablets, iPads, and JupyterLite/Pyodide environments.

Both simulators use frozen dataclass containers and provide multi-format presentation suites (.summary(), .plot(), .to_markdown(), .to_latex(), .to_typst()).

Architecture & Simulation Dataflow

flowchart TD
    subgraph Inputs["1. Calibration & Shock Inputs"]
        ICIO["ICIO tables by year\n(bundled: OECD ICIO 2019,\nG20 + ROW x 20 ISIC sections)"]
        PRESETS["Pre-calibrated Presets\n('nafta_china', 'symmetric_3c')"]
        SHOCKS["Policy Shocks\n(Tariffs, Rate Hikes, Fiscal Transfers)"]
    end

    subgraph Solvers["2. Quantitative General Equilibrium Engines"]
        direction TB
        TPS["TradePolicySimulator\n(Caliendo & Parro 2015)\n- Eaton-Kortum Gravity Contraction\n- Leontief Intermediate I-O System\n- Wage Tatonnement Market Clearing (< 1e-6)"]
        MTS["MonetaryTransmissionSimulator\n(Kaplan, Moll & Violante 2018)\n- Stationary Wealth Distribution\n- Sequence-Space Jacobians J^{C,r}, J^{C,Y}\n- GE Feedback (NKPC & Taylor Rule)"]
    end

    subgraph Results["3. Immutable Simulation Results"]
        TPR["TradePolicySimulationResult\n- Proportional Changes (w, P, CPI)\n- Terms of Trade & Trade Diversion\n- 3-Way Welfare Decomposition"]
        MTR["MonetaryTransmissionResult\n- IRF Paths (Y, C, pi, r)\n- 10-Decile MPC Ladder\n- KMV Direct vs. Indirect Channel Share"]
    end

    subgraph Delivery["4. Dual-Delivery Reporting & Labs"]
        EXPORT["Academic Reporting Suites\n- .summary() / .sector_summary()\n- .to_markdown() / .to_latex() / .to_typst()\n- Matplotlib Publication Figures (.plot())"]
        LABS["Browser-Native WebAssembly / Canvas Labs\n- curso/site/labs/comercio-aranceles.html\n- curso/site/labs/politica-monetaria-hank.html"]
    end

    Inputs --> Solvers
    Solvers --> Results
    Results --> Delivery

1. Quantitative Trade Policy Simulator (`TradePolicySimulator`)

1.1 Theoretical Framework: Caliendo & Parro (2015)

Consider a global economy with $N$ countries ($n, i \in \{1, \dots, N\}$) and $J$ sectors ($j, k \in \{1, \dots, J\}$). Production in country $n$ and sector $j$ combines labor $L_{n, j}$ with intermediate input bundles from all sectors under constant returns to scale.

Using Exact Hat Algebra (Dekle, Eaton, and Kortum 2007; Caliendo and Parro 2015), counterfactual equilibrium changes in wages, prices, and trade flows are expressed in proportional terms $\hat{x} \equiv x' / x$, bypassing the need to estimate unobserved structural productivities or baseline iceberg trade costs.

Given counterfactual bilateral gross tariff changes $\hat{\tau}_{ni}^j = \frac{1 + t_{ni}'^j}{1 + t_{ni}^j}$:

Unit Cost Changes: $$\hat{c}_n^j = \hat{w}_n^{\gamma_n^j} \prod_{k=1}^J \left( \hat{P}_n^k \right)^{\gamma_n^{j, k}}$$ where $\gamma_n^j > 0$ is the value-added share and $\gamma_n^{j, k} \ge 0$ is the intermediate input cost share ($\gamma_n^j + \sum_{k=1}^J \gamma_n^{j, k} = 1$).
Sectoral Price Indices (Nonlinear Contraction Mapping): $$\hat{P}_n^j = \left( \sum_{i=1}^N \pi_{ni}^j \left( \hat{c}_i^j \hat{\tau}_{ni}^j \right)^{-\theta_j} \right)^{-\frac{1}{\theta_j}}$$ where $\pi_{ni}^j$ is the baseline trade share of country $n$ on imports from country $i$ in sector $j$, and $\theta_j > 0$ is the sectoral trade elasticity.
Counterfactual Bilateral Trade Shares: $$\pi_{ni}'^j = \pi_{ni}^j \left( \frac{\hat{c}_i^j \hat{\tau}_{ni}^j}{\hat{P}_n^j} \right)^{-\theta_j}$$
Leontief Intermediate Input-Output Expenditure System: Given counterfactual tariffs $\tau'$, trade shares $\pi'$, and wages $\hat{w}$, total expenditure $X_n'^j$ solves the linear system: $$X_n'^j = \alpha_n^j \left( \hat{w}_n w_n L_n + R_n' + D_n \right) + \sum_{k=1}^J \gamma_n^{k, j} \sum_{m=1}^N \frac{\pi_{mn}'^k}{\tau_{mn}'^k} X_m'^k$$ where $\alpha_n^j$ is the final consumption expenditure share, $D_n$ is the national trade deficit, and $R_n'$ is counterfactual tariff revenue: $$R_n' = \sum_{j=1}^J \sum_{i=1}^N \frac{\tau_{ni}'^j - 1}{\tau_{ni}'^j} \pi_{ni}'^j X_n'^j$$
Goods and Factor Market Clearing: Gross sectoral output equals world demand: $$Y_n'^j = \sum_{m=1}^N \frac{\pi_{mn}'^j}{\tau_{mn}'^j} X_m'^j$$ Equilibrium wages $\hat{w}_n$ are solved iteratively via damped tatonnement until excess factor demand satisfies: $$\max_n \left| \sum_{j=1}^J \gamma_n^j Y_n'^j - \hat{w}_n w_n L_n \right| < 10^{-6}$$

1.2 Terms of Trade & Descriptive Welfare Components

The terms-of-trade index change $\widehat{\text{ToT}}_n \equiv \hat{P}_n^X / \hat{P}_n^M$ reflects the price of country $n$'s exports relative to its imports: - Export Price Index Change: $\hat{P}_n^X = \sum_j \sum_{m \ne n} \omega_{n, m}^j \hat{c}_n^j$ - Import Price Index Change: $\hat{P}_n^M = \sum_j \sum_{m \ne n} \mu_{n, m}^j (\hat{c}_m^j \hat{\tau}_{nm}^j)$

The welfare_decomposition dictionary reports two domestic-share statistics over tradable sectors $\mathcal{T}$:

\[S_n = \sum_{j \in \mathcal{T}} \frac{\alpha_n^j}{\theta_j} \ln\left(\frac{\pi_{nn}^j}{\pi_{nn}'^j}\right), \qquad M_n = \sum_{j \in \mathcal{T}} \frac{\alpha_n^j(1-\gamma_n^j)}{\gamma_n^j\theta_j} \ln\left(\frac{\pi_{nn}^j}{\pi_{nn}'^j}\right).\]

The existing keys are terms_of_trade for $S_n$ and input_output for $M_n$. The key total is $\Delta\ln\mathcal{W}_n=\ln(\hat I_n/\hat P_n)$, and tariff_revenue is defined as the residual $\Delta\ln\mathcal{W}_n-S_n-M_n$. The three entries add to total by construction; this does not identify three separate economic mechanisms. In particular, terms_of_trade is not the log of the separately computed terms_of_trade_hat index, and tariff_revenue is not an independently computed fiscal welfare effect. Use tariff_revenue_prime for actual counterfactual receipts. These dictionary values are in log-change units, while welfare_pct is a percentage change.

1.3 Python Usage & Counterfactual Recipes

Recipe 1: Bilateral Tariff & Trade Diversion (USA vs China)

import numpy as np
from puremacro.models import TradePolicySimulator

# Load pre-calibrated 3-country, 2-sector model (MEX, USA, CHN)
sim = TradePolicySimulator.from_preset("nafta_china")

# Inspect dimensions and identifiers
print(f"Countries: {sim.country_codes} (N={sim.N}), Sectors: {sim.sector_codes} (J={sim.J})")

# Simulate a unilateral 25% tariff by the US on Chinese manufactures
res = sim.simulate_bilateral_tariff(
    importer="USA",
    exporter="CHN",
    tariff_rate=0.25,
    sector="Manufactures",
    tol=1e-10,
)

print(repr(res))
# Output: TradePolicySimulationResult(countries=['MEX', 'USA', 'CHN'], sectors=['Manufactures', 'Services'], status='converged', iterations=37, residual=6.08e-07)
# `residual` is in levels (flows of about 3e4); tol=1e-10 is relative to each country's labour income.

# Country-level summary
print(res.summary())

# Access specific country outcomes via code or indexing
usa_results = res["USA"]
mex_results = res.country("MEX")
print(f"US Tariff Revenue: {usa_results['tariff_revenue_prime']:.2f}")
print(f"Mexico Welfare Gain (Trade Diversion): +{mex_results['welfare_pct']:.3f}%")

Recipe 2: Reciprocal Multi-Party Trade War

# Simulate an escalating trade war between Coalition A (USA) and Coalition B (MEX, CHN)
res_war = sim.simulate_trade_war(
    coalition_a=["USA"],
    coalition_b=["MEX", "CHN"],
    tariff_rate_a=0.30,   # US imposes 30% tariffs on both
    tariff_rate_b=0.20,   # Retaliatory 20% tariff on the US
    tol=1e-10,
)

# Export LaTeX table for paper inclusion
print(res_war.to_latex())

# Generate publication-quality bar chart of welfare impacts
fig = res_war.plot(kind="welfare", figsize=(8.0, 4.5))
fig.savefig("trade_war_welfare.png", dpi=300)

2. Monetary & Macroprudential Transmission Simulator (`MonetaryTransmissionSimulator`)

2.1 Theoretical Framework: HANK vs. RANK

Textbook Representative-Agent New Keynesian (RANK) models dictate that monetary policy transmits almost exclusively via the direct intertemporal substitution channel: identical unconstrained households adjust savings along a single Euler equation ($c_t = \mathbb{E}_t c_{t+1} - \frac{1}{\gamma} (i_t - \mathbb{E}_t \pi_{t+1})$).

In contrast, Heterogeneous-Agent New Keynesian (HANK) models (Kaplan, Moll, and Violante 2018; Auclert et al. 2021) incorporate uninsurable idiosyncratic productivity risk and borrowing constraints. A large share of households are hand-to-mouth (either poor hand-to-mouth with zero net worth, or wealthy hand-to-mouth with illiquid assets), exhibiting high marginal propensities to consume (MPCs) out of current income.

Consequently, the indirect general equilibrium channel—transmitted through labor demand, aggregate output, and disposable income—drives the dominant share of consumption movements.

2.2 Kaplan-Moll-Violante (2018) Sequence-Space Decomposition

Using the sequence-space Jacobian methodology (Auclert, Bardóczy, Rognlie, and Straub 2021), the aggregate consumption response vector $d\mathbf{C} \in \mathbb{R}^T$ over horizon $T$ is decomposed into:

\[d\mathbf{C} = \underbrace{\mathbf{J}^{C, r} \, d\mathbf{r}}_{\text{Direct Channel (Substitution)}} + \underbrace{\mathbf{J}^{C, Y} \, d\mathbf{Y}}_{\text{Indirect Channel (GE Income)}}\]

where $\mathbf{J}^{C, r} \equiv \frac{\partial \mathbf{C}}{\partial \mathbf{r}}$ and $\mathbf{J}^{C, Y} \equiv \frac{\partial \mathbf{C}}{\partial \mathbf{Y}}$ are $T \times T$ sequence-space Jacobian matrices.

In general equilibrium, the New Keynesian Phillips Curve and Taylor rule define: $$d\boldsymbol{\pi} = \mathbf{K}_\pi d\mathbf{Y}, \quad d\mathbf{r} = \mathbf{M}_{r, Y} d\mathbf{Y} + d\boldsymbol{\epsilon}$$

Substituting into the household block yields the sequence-space general equilibrium solve: $$\left( \mathbf{I} - \mathbf{J}^{C, Y} - \mathbf{J}^{C, r} \mathbf{M}_{r, Y} \right) d\mathbf{Y} = \mathbf{J}^{C, r} d\boldsymbol{\epsilon}$$

Dimension	RANK Economy	HANK Economy
Market Structure	Complete asset markets	Incomplete markets, borrowing constraint $a' \ge 0$
Household MPCs	Uniform across deciles: $1 - \beta \approx 0.015$	Steep gradient: Decile 1 ($>0.40$) to Decile 10 ($<0.06$)
Direct Channel Share	Identically $100\%$	$20\% - 40\%$
Indirect Channel Share	Identically $0\%$	$60\% - 80\%$
Propagation Core	Intertemporal Euler equation	General equilibrium labor income multiplier

2.3 Python Usage & Transmission Recipes

Recipe 3: Monetary Policy Tightening (25 bps Hike)

from puremacro.models import MonetaryTransmissionSimulator

# Initialize simulator with continuous asset grid parameters
sim_mon = MonetaryTransmissionSimulator(
    beta=0.985,
    gamma=1.0,
    r_ss=0.01,
    phi_pi=1.5,
    kappa=0.1,
    n_a=50,
    a_max=30.0,
)

# Steady-state aggregate MPC
print(f"HANK Steady-State MPC: {sim_mon.steady_state_mpc:.4f}")

# Simulate a 25 bps rate hike with persistence rho=0.7 over 40 quarters
res_mon = sim_mon.simulate_rate_shock(magnitude=0.0025, rho=0.7, T=40)

print(repr(res_mon))
# Output: MonetaryTransmissionResult(shock_type='rate', magnitude=+0.0025, T=40, mpc_hank=0.1582, mpc_rank=0.0150, indirect_share_hank=68.4%)

# Display comprehensive side-by-side summary
print(res_mon.summary())

# Extract peak responses
peaks = res_mon.peak_responses()
print(f"HANK Peak Output Contraction: {peaks['output_hank']*100:.3f}%")
print(f"RANK Peak Output Contraction: {peaks['output_rank']*100:.3f}%")

# 4-panel publication visualization (IRFs, MPC ladder, KMV channels)
fig = res_mon.plot(kind="all", figsize=(12.0, 8.5))
fig.savefig("monetary_transmission_hank_rank.png", dpi=300)

Recipe 4: Targeted Balance-Sheet & Fiscal Transfers

# Simulate a targeted cash transfer to liquidity-constrained hand-to-mouth households
res_tf = sim_mon.simulate_balance_sheet_intervention(
    amount=1.0,
    target="borrowers",   # Poorest 25% of households
    T=40,
)

print(f"Initial Consumption Multiplier in HANK: {res_tf.irf_consumption_hank[0]:.4f}")
print(f"Initial Consumption Multiplier in RANK: {res_tf.irf_consumption_rank[0]:.4f}")
# In HANK, high-MPC borrowers spend immediately (>10x the response in RANK)

3. Comprehensive API Reference

3.1 `TradePolicySimulator`

Method / Property	Signature	Description
`country_codes`	`tuple[str, ...]` (property)	Tuple of country identifier codes in the model.
`sector_codes`	`tuple[str, ...]` (property)	Tuple of sector identifier codes in the model.
`N`	`int` (property)	Number of countries in the trade model.
`J`	`int` (property)	Number of sectors in the trade model.
`from_preset(name)`	`name: str = "nafta_china"`	Instantiate simulator from pre-calibrated baseline (`'nafta_china'`, `'symmetric_3c'`).
`from_icio(year, *, tables=None)`	`year: int`, `tables: {year: table}`	Calibrate a Caliendo-Parro simulator to the ICIO table of `year` (trade shares, input-output cost shares, final-expenditure shares, value added and trade deficits read from the table). Without `tables` only the bundled 2019 table is available (OECD ICIO 2023 edition, G20 countries + rest of the world, 20 ISIC Rev.4 sections); any other year raises `ValueError` listing the available years.
`simulate_bilateral_tariff()`	`importer, exporter, tariff_rate, sector=None, tol=1e-10`	Simulate ad-valorem bilateral tariff shock with market clearing verification.
`simulate_trade_war()`	`coalition_a, coalition_b, tariff_rate_a, tariff_rate_b=None`	Simulate bilateral or multi-party trade war between two non-overlapping coalitions.
`simulate_arbitrary_tariffs()`	`tariffs_new: np.ndarray, tol=1e-10`	Simulate arbitrary gross bilateral tariff matrix $\tau_{ni}'^j$ of shape `(J, N, N)`.
`simulate_tariff_counterfactual()`	`tariff_shocks: dict \\| np.ndarray \\| None`	Flexible counterfactual interface accepting dict mappings or array inputs.

3.2 `TradePolicySimulationResult`

Attribute / Method	Type / Signature	Description
`w_hat`	`np.ndarray (N,)`	Proportional change in nominal wages $\hat{w}_n = w_n' / w_n$.
`P_hat`	`np.ndarray (N, J)`	Proportional change in sectoral price indices $\hat{P}_n^j$.
`P_index_hat`	`np.ndarray (N,)`	Proportional change in consumer price indices $\hat{P}_n$.
`real_wage_hat`	`np.ndarray (N,)`	Proportional change in real wages $\hat{w}_n / \hat{P}_n$.
`terms_of_trade_hat`	`np.ndarray (N,)`	Proportional change in terms of trade index $\hat{P}_n^X / \hat{P}_n^M$.
`welfare_pct`	`np.ndarray (N,)`	Percentage change in real income / welfare $(\hat{\mathcal{W}}_n - 1) \times 100$.
`tariff_revenue_prime`	`np.ndarray (N,)`	Counterfactual tariff revenue collection by country $R_n'$.
`welfare_decomposition`	`dict[str, np.ndarray]`	Domestic-share statistics `'terms_of_trade'` and `'input_output'`, residual `'tariff_revenue'`, and log-welfare `'total'`; see section 1.2.
`market_clearing_residual`	`float`	Maximum goods and factor market clearing residual across countries, in the units of the baseline flows ($< 10^{-6}$ on convergence). The `tol` argument is relative to each country's labour income, so this absolute residual can exceed `tol`.
`converged`	`bool`	Whether wage tatonnement converged within tolerance. A `RuntimeWarning` is issued when the solver stops at `max_iter` without converging.
`country(code)`	`code: str \\| int -> pd.Series`	Return the equilibrium outcome series for a specific country.
`__getitem__(key)`	`key: str \\| int -> pd.Series`	Subscript access delegating to `country(key)`.
`summary()`	`-> pd.DataFrame`	Country-level summary table of equilibrium changes.
`sector_summary()`	`-> pd.DataFrame`	Detailed country-sector breakdown of prices, expenditures, and gross outputs.
`to_markdown()`	`index=True, digits=4 -> str`	Format summary table as GitHub-flavored Markdown.
`to_latex()`	`index=True, digits=4 -> str`	Format summary table as LaTeX tabular environment.
`to_typst()`	`index=True, digits=4 -> str`	Format summary table as Typst table.
`plot(kind)`	`kind='welfare' \\| 'real_wage' \\| 'terms_of_trade' \\| 'wage' \\| 'cpi'`	Plot counterfactual bar chart by country.

3.3 `MonetaryTransmissionSimulator`

Method / Property	Signature	Description
`steady_state_mpc`	`float` (property)	Steady-state aggregate quarterly MPC in HANK economy.
`simulate_rate_shock()`	`magnitude=0.0025, rho=0.7, T=40`	Solve HANK vs. RANK sequence-space equilibrium for an interest rate shock.
`simulate_balance_sheet_intervention()`	`amount=1.0, target='borrowers', T=40`	Simulate targeted fiscal transfer or balance-sheet injection.
`simulate_transmission()`	`shock_type='rate', shock_path=None, ...`	General simulation entry point supporting custom exogenous shock paths.

3.4 `MonetaryTransmissionResult`

Attribute / Method	Type / Signature	Description
`irf_output_hank / rank`	`np.ndarray (T,)`	Output impulse response paths $d\mathbf{Y}$.
`irf_consumption_hank / rank`	`np.ndarray (T,)`	Consumption impulse response paths $d\mathbf{C}$.
`irf_inflation_hank / rank`	`np.ndarray (T,)`	Annualized inflation impulse response paths $d\boldsymbol{\pi}$.
`irf_rate_hank / rank`	`np.ndarray (T,)`	Ex-ante real interest rate paths $d\mathbf{r}$.
`mpc_deciles_hank / rank`	`pd.Series (10,)`	Marginal propensity to consume across 10 wealth deciles.
`aggregate_mpc_hank / rank`	`float`	Aggregate quarterly economy-wide MPC.
`direct_channel_hank`	`np.ndarray (T,)`	KMV direct intertemporal substitution channel $\mathbf{J}^{C, r} d\mathbf{r}$.
`indirect_channel_hank`	`np.ndarray (T,)`	KMV indirect general equilibrium labor income channel $\mathbf{J}^{C, Y} d\mathbf{Y}$.
`indirect_share_hank`	`float`	Percentage share of date-0 consumption driven by indirect GE channel.
`peak_responses()`	`-> dict[str, float]`	Peak responses for output, inflation, and consumption.
`summary()`	`-> str`	Formatted ASCII report comparing HANK and RANK mechanisms.
`to_frame()`	`-> pd.DataFrame`	Tidy quarterly path DataFrame indexed by quarter $t \in [0, T-1]$.
`plot(kind)`	`kind='all' \\| 'irf' \\| 'mpc' \\| 'kmv'`	Comparative HANK vs. RANK transmission figures.

4. Interactive WebAssembly / Browser Labs (`curso/site/labs/`)

In addition to the Python library, puremacro bundles two fully interactive, client-side browser laboratories located in curso/site/labs/. Built using vanilla HTML5, CSS3, KaTeX, and HTML Canvas, they require zero compilation, zero node_modules, and operate 100% offline.

4.1 `comercio-aranceles.html` & `.js` (Trade War & Nearshoring Lab)

Path: curso/site/labs/comercio-aranceles.html and curso/site/labs/comercio-aranceles.js.
Core Features:
Real-time general equilibrium solver in client-side JavaScript executing in $<2$ ms per slider adjustment.
Interactive bilateral tariff controls: US on China, US on Mexico, Mexican retaliation, Chinese retaliation.
Illustrative domestic-share and input-output statistics with a residual that makes the displayed components add to the percentage welfare change. The chart's channel labels do not establish a structural decomposition; its percentage scale also differs from the Python dictionary's log-change scale.
Visualizer for trade diversion and nearshoring effects toward Mexican manufacturing.
Pre-configured policy presets: Baseline, US–China Trade War, Nearshoring Boom, All-Out Trade War.

4.2 `politica-monetaria-hank.html` & `.js` (HANK vs. RANK Transmission Lab)

Path: curso/site/labs/politica-monetaria-hank.html and curso/site/labs/politica-monetaria-hank.js.
Core Features:
Comparative impulse response curves for output, inflation, and consumption.
Interactive parameter sliders for shock size, persistence ($\rho$), and the share of hand-to-mouth households.
Live bar charts contrasting the steep empirical MPC decile ladder against the flat representative-agent benchmark.
Dynamic visualizer for the Kaplan-Moll-Violante direct versus indirect transmission shares.

4.3 Running Locally

To run the interactive laboratories locally without any external dependencies:

# From the puremacro repository root
cd curso/site
python3 -m http.server 8000

Open your browser to http://localhost:8000/labs/comercio-aranceles.html or http://localhost:8000/labs/politica-monetaria-hank.html.

References

Auclert, A., Bardóczy, B., Rognlie, M., and Straub, L. (2021). "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models." Econometrica, 89(6), 3115–3148.
Caliendo, L. and Parro, F. (2015). "Estimates of the Trade and Welfare Effects of NAFTA." The Review of Economic Studies, 82(1), 1–44.
Dekle, R., Eaton, J., and Kortum, S. (2007). "Unbalanced Trade." American Economic Review, 97(2), 351–355.
Eaton, J. and Kortum, S. (2002). "Technology, Geography, and Trade." Econometrica, 70(5), 1741–1779.
Kaplan, G., Moll, B., and Violante, G. L. (2018). "Monetary Policy According to HANK." American Economic Review, 108(3), 697–743.

Attribute / Method	Type / Signature	Description
`w_hat`	`np.ndarray (N,)`	Proportional change in nominal wages \(\hat{w}_n = w_n' / w_n\).
`P_hat`	`np.ndarray (N, J)`	Proportional change in sectoral price indices \(\hat{P}_n^j\).
`P_index_hat`	`np.ndarray (N,)`	Proportional change in consumer price indices \(\hat{P}_n\).
`real_wage_hat`	`np.ndarray (N,)`	Proportional change in real wages \(\hat{w}_n / \hat{P}_n\).
`terms_of_trade_hat`	`np.ndarray (N,)`	Proportional change in terms of trade index \(\hat{P}_n^X / \hat{P}_n^M\).
`welfare_pct`	`np.ndarray (N,)`	Percentage change in real income / welfare \((\hat{\mathcal{W}}_n - 1) \times 100\).
`tariff_revenue_prime`	`np.ndarray (N,)`	Counterfactual tariff revenue collection by country \(R_n'\).
`welfare_decomposition`	`dict[str, np.ndarray]`	Domestic-share statistics `'terms_of_trade'` and `'input_output'`, residual `'tariff_revenue'`, and log-welfare `'total'`; see section 1.2.
`market_clearing_residual`	`float`	Maximum goods and factor market clearing residual across countries, in the units of the baseline flows (\(< 10^{-6}\) on convergence). The `tol` argument is relative to each country's labour income, so this absolute residual can exceed `tol`.
`converged`	`bool`	Whether wage tatonnement converged within tolerance. A `RuntimeWarning` is issued when the solver stops at `max_iter` without converging.
`country(code)`	`code: str \\| int -> pd.Series`	Return the equilibrium outcome series for a specific country.
`__getitem__(key)`	`key: str \\| int -> pd.Series`	Subscript access delegating to `country(key)`.
`summary()`	`-> pd.DataFrame`	Country-level summary table of equilibrium changes.
`sector_summary()`	`-> pd.DataFrame`	Detailed country-sector breakdown of prices, expenditures, and gross outputs.
`to_markdown()`	`index=True, digits=4 -> str`	Format summary table as GitHub-flavored Markdown.
`to_latex()`	`index=True, digits=4 -> str`	Format summary table as LaTeX tabular environment.
`to_typst()`	`index=True, digits=4 -> str`	Format summary table as Typst table.
`plot(kind)`	`kind='welfare' \\| 'real_wage' \\| 'terms_of_trade' \\| 'wage' \\| 'cpi'`	Plot counterfactual bar chart by country.

Attribute / Method	Type / Signature	Description
`irf_output_hank / rank`	`np.ndarray (T,)`	Output impulse response paths \(d\mathbf{Y}\).
`irf_consumption_hank / rank`	`np.ndarray (T,)`	Consumption impulse response paths \(d\mathbf{C}\).
`irf_inflation_hank / rank`	`np.ndarray (T,)`	Annualized inflation impulse response paths \(d\boldsymbol{\pi}\).
`irf_rate_hank / rank`	`np.ndarray (T,)`	Ex-ante real interest rate paths \(d\mathbf{r}\).
`mpc_deciles_hank / rank`	`pd.Series (10,)`	Marginal propensity to consume across 10 wealth deciles.
`aggregate_mpc_hank / rank`	`float`	Aggregate quarterly economy-wide MPC.
`direct_channel_hank`	`np.ndarray (T,)`	KMV direct intertemporal substitution channel \(\mathbf{J}^{C, r} d\mathbf{r}\).
`indirect_channel_hank`	`np.ndarray (T,)`	KMV indirect general equilibrium labor income channel \(\mathbf{J}^{C, Y} d\mathbf{Y}\).
`indirect_share_hank`	`float`	Percentage share of date-0 consumption driven by indirect GE channel.
`peak_responses()`	`-> dict[str, float]`	Peak responses for output, inflation, and consumption.
`summary()`	`-> str`	Formatted ASCII report comparing HANK and RANK mechanisms.
`to_frame()`	`-> pd.DataFrame`	Tidy quarterly path DataFrame indexed by quarter \(t \in [0, T-1]\).
`plot(kind)`	`kind='all' \\| 'irf' \\| 'mpc' \\| 'kmv'`	Comparative HANK vs. RANK transmission figures.