Advanced Macroeconomics · Leer en español →
ITAM · FALL 2026 · JORGE ALONSO-ORTIZ
Advanced Macroeconomics
A course on business cycles: what starts them, how they spread, and what households, firms and governments do about them. Data, econometrics and general-equilibrium models, with the tools used at central banks, consultancies and international organizations.
8
units, each with its slide deck
8
interactive labs in the browser
31
notebooks in Spanish and English, soon in Colab
0
licences: everything in Python with puremacro
Three questions organize the course
WHAT STARTS THEM?
Measure and decompose
National accounts, filters, cycle moments and wedge accounting: what needs explaining before proposing a model.
HOW DO THEY SPREAD?
Identify shocks
SVARs, narrative evidence and local projections, honest about which assumption produces each result.
WHAT DO AGENTS DO?
Model decisions
From the neoclassical model to RBC, from heterogeneous agents and HANK to sticky prices and labor-market search.
Units
Each unit opens with a claim the material defends: the slides argue it, the lab lets you explore it and the notebooks reproduce it with data. Slides are in Spanish.
BLOCK A · GROWTH AND FLUCTUATIONS WITHOUT FRICTIONS
UNIT A1 · WEEKS 1–2
Stylized facts: measuring the cycle
GDP is a revisable construct, and the filter you choose decides the cycle. Kaldor’s facts organize the long run; the labor share and TFP are the ones that are breaking.
Slides 1 (PDF)
Lab: How much of the cycle comes from the filter?
Notebooks: T01_A · T01_B · T01_C (coming soon to Colab)
UNIT A2 · WEEKS 3–4
The neoclassical growth model
Planner and market deliver the same allocation, and the parameters pin down the long run. Solving the model means choosing an approximation: local by perturbation, or global with the value function.
Slides 2 (PDF)
Lab: How fast does an economy reach its steady state?
Notebooks: T02_A (coming soon to Colab)
UNIT A3 · WEEK 5
Risk and uncertainty
Risk is modeled with states of nature, and the full risk sharing that implies is rejected by the data.
Slides 3 (PDF)
Lab: What is lost when an AR(1) becomes a Markov chain?
Notebooks: T03_A · T03_B · T03_C · T03_D · T03_E · T03_F (coming soon to Colab)
UNIT A4 · WEEKS 7–8
Real business cycles and business-cycle accounting
The cycle is summarized by second moments, and a calibrated RBC matches only some of them. Every shock can be quantified, and the wedges point to the equation of the model that fails.
Slides 4 (PDF)
Lab: Can productivity shocks alone explain the business cycle?
Notebooks: T02_B · T04_A · T04_C (coming soon to Colab)
UNIT A5 · WEEKS 9–10
Empirical identification: SVARs, narrative evidence and local projections
Identifying a shock requires an assumption, and the assumption decides the result. Narrative methods and local projections change the identifying assumption; they don’t remove it.
Slides 5 (PDF)
Lab: What does a rate hike do to output?
Notebooks: T05_A · T05_B · T05_C · T05_D · T05_E · T05_F (coming soon to Colab)
UNIT A6 · WEEK 11
Incomplete markets, heterogeneity and HANK
When the aggregate state is a distribution, the multiplier is no longer a free parameter.
Slides 6 (PDF)
Lab: What happens when no one can insure their income?
Notebooks: T06_A · T06_B · T06_C (coming soon to Colab)
BLOCK B · MODELS WITH FRICTIONS
UNIT B1 · WEEKS 13–14
Mechanisms: labor, capital and the small open economy
The macro labor-supply elasticity is produced by aggregation, not by microeconometrics. Variable utilization, adjustment costs and openness account for the part of the cycle that productivity does not.
UNIT B2 · WEEKS 15–16
Non-competitive markets: sticky prices and labor-market frictions
With sticky prices money stops being neutral and the Phillips curve gets a slope. With search frictions unemployment is an equilibrium, not a residual.
Slides 8 (PDF)
Lab: How much output does it cost to bring inflation down?
Lab: Why is there unemployment when there are vacancies?
Notebooks: T02_C · T02_D · T08_A (coming soon to Colab)
IN THE BROWSER, NOTHING TO INSTALL
Interactive labs
Move a parameter and the model is solved again in front of you. Each lab comes with guided experiments, the math behind it and links to the notebook that reproduces it in Python.
Notebooks
The course notebooks are being published over the next few days. Each will open in Google Colab with one click, install puremacro and download only the data it needs.
| Notebook | Topic |
|---|---|
| T00 | Course map and environment check |
| T01_A | Business-cycle facts from the labor side: HP, Hamilton, Okun and Beveridge |
| T01_B | National accounts across 49 countries |
| T01_C | High-frequency indicators and uncertainty |
| T02_A | Neoclassical growth: local and global solutions |
| T02_B | The RBC model from a .mod file |
| T02_C | The three-equation New Keynesian model |
| T02_D | The zero lower bound with OccBin |
| T02_E | Bayesian estimation of a DSGE model |
| T02_F | Macro-finance: Gertler–Karadi |
| T03_A | Aggregate shocks |
| T03_B | Volatility: GARCH and DCC |
| T03_C | Growth at risk |
| T03_D | Uncertainty indices from text |
| T03_E | Uncertainty with local language models |
| T03_F | Central-bank narratives with language models |
| T04_A | The tax multiplier three ways |
| T04_C | Business-cycle accounting: the four wedges |
| T05_A | SVAR identification |
| T05_B | Local projections |
| T05_C | Staggered difference-in-differences |
| T05_D | LP-DiD |
| T05_E | Generalized impulse responses by regime |
| T05_F | An SVAR of post-pandemic inflation |
| T06_A | Wealth inequality: Aiyagari–Huggett |
| T06_B | Life cycle and demographics |
| T06_C | Portfolios and Epstein–Zin preferences |
| T07_A | Firm dynamics: Hopenhayn |
| T08_A | Labor flows: Shimer and Mexico’s four-state ENOE |
| T09_A | Validation gallery |
| T09_B | Build your own index |
Calendar
One session a week. Day, time, room and any changes are announced on Canvas, the course’s official channel.
| Wk. | Dates | Content | Due |
|---|---|---|---|
| 1 | Aug 10–14 | National accounts, real-time data and revisions; Hodrick–Prescott and Hamilton filters. | — |
| 2 | Aug 17–21 | Kaldor facts beyond the US; measuring capital, the labor share, TFP and intangibles; the Beveridge curve. | Homework 1 out |
| 3 | Aug 24–28 | Neoclassical model: planner, competitive equilibrium, steady state and balanced growth path. | — |
| 4 | Aug 31–Sep 4 | Log-linearization, simulating .mod models, the Bellman equation and value function iteration. | — |
| 5 | Sep 7–11 | Arrow–Debreu markets; Tauchen and Rouwenhorst; the endogenous grid method; stochastic volatility and growth at risk. | Homework 1 due |
| — | Sep 14–18 | Mexican Independence week: no session on Wednesday 16. | — |
| 6 | Sep 21–25 | First midterm (Wednesday 23), units A1–A3. | Midterm 1 |
| 7 | Sep 28–Oct 2 | Real business cycles: Kydland–Prescott moments, the RBC model and its calibration. | — |
| 8 | Oct 5–9 | Hansen’s indivisible labor; a taxonomy of shocks; Chari–Kehoe–McGrattan business-cycle accounting. | Homework 2 out |
| 9 | Oct 12–16 | VARs, Cholesky and Blanchard–Quah; the evidence on technology shocks (Galí 1999, Fisher 2006). | — |
| 10 | Oct 19–23 | Narrative methods, proxy SVARs and monetary shocks (including Banxico announcements); local projections and state-dependent multipliers. | Homework 2 due |
| 11 | Oct 26–30 | Precautionary saving, Aiyagari–Huggett, the wealth distribution, marginal propensities to consume and HANK. | Homework 3 and the AI audit out |
| 12 | Nov 2–6 | Second midterm (Wednesday 4), units A4–A5. | Midterm 2 |
| 13 | Nov 9–13 | Labor supply over the cycle: indivisible labor, extensive and intensive margins, home production. | Homework 3 due; final project out |
| 14 | Nov 16–20 | Variable utilization, adjustment costs and Tobin’s q; the small open economy (Aguiar–Gopinath). | Homework 4 out; AI audit due (Fri 20) |
| 15 | Nov 23–27 | Monopolistic competition and markups; price rigidity, monetary non-neutrality and the 2021–2023 inflation. | — |
| 16 | Nov 30–Dec 2 | Mortensen–Pissarides, the Shimer puzzle and the Hosios condition; Mexico’s dual labor market (ENOE, F/I/U/N). | Homework 4 due; project presentation |
| — | Dec 7–19 | In-class theory final (B1–B2) and take-home computational final (A6 and the empirical part). | Finals |
Grading
| Component | Weight |
|---|---|
| Four programming homeworks | 20% |
| Adversarial audit of AI-generated code | 10% |
| Final project | 10% |
| First midterm (A1–A3) | 15% |
| Second midterm (A4–A5) | 15% |
| Theory final (B1–B2) | 15% |
| Computational final (A6 and empirical part) | 15% |
HOW WE WORK
All in Python, nothing to license
The course’s models, estimates and figures run on puremacro, a pure-Python library that also reads Dynare .mod files. It works in Colab, on a laptop and even on an iPad.
pip install puremacroHomework includes an adversarial audit: finding the bugs in code written by an AI. Reading code with suspicion is part of the craft.
Jorge Jesus Alonso Ortiz 