Motivation In the standard neoclassical model, three return measures coincide: $$ R^{rf} = \mathbb{E}R^k_{t+1} = MPK. $$ This is a strong and testable prediction. Empirically, all three diverge and move differently over time. The question is: what drives the wedges? The answer: risk drives a wedge between $R^{rf}$ and $\mathbb{E}R^k$; markups drive a wedge between $\mathbb{E}R^k$ and $MPK$. Model A representative household with CRRA preferences $\mathbb{E}_0\sum_t \beta^t \frac{C_t^{1-\sigma}}{1-\sigma}$, inelastic labor supply $\bar N$. ...

We will analyze a representative agent economy and see how shocks and intertemporal discounted tradeoff drives asset prices. Stochastic Environment Primitives. Time is discrete, $t = 0, 1, 2, \ldots$ A random variable $s_t$ (the “state”) summarizes all exogenous shocks at date $t$. The initial state $s_0$ is known. Histories. A history of shocks through date $t$ is the tuple $$s^t = (s_0, s_1, \ldots, s_t).$$We write $s^{t+1} \geq s^t$ to mean that $s^t$ is a prefix of $s^{t+1}$ (i.e., the first $t+1$ entries of $s^{t+1}$ coincide with $s^t$). ...

Finally! Let’s talk about shocks. Motivation U.S. per capita real GDP (in logs) exhibits a clear upward trend over the 20th century. The neoclassical growth model explains this trend well. However, GDP also fluctuates around this trend. The question is: can the same neoclassical framework, driven solely by productivity shocks, also explain these cyclical fluctuations? To isolate cycles, we decompose log GDP into a trend component (extracted via the Hodgkin–Prescott filter or a band-pass filter) and a cyclical component (the residual). The cyclical component reveals the following stylized facts from U.S. quarterly data (1947–1995, HP-filtered): ...

Lecture 9 measures misallocation but under a simple market structure (intermediate firms -> final firms -> production). Baqaee and Farhi (2020) extended the analysis to arbitrary production network. With general CRS technology assumption, one can decompose GDP growth into contributions from technology, factor accumulation, and misallocation—all using observable data. Environment Primitives There are three classes of agents: $N$ producers (firms), indexed $i = 1, \dots, N$. $F$ primary factors, indexed $f = 1, \dots, F$, supplied inelastically in quantities $\bar{L}_f > 0$ (exogenous). A representative consumer and a final good sector. The exogenous objects are: productivities $\{A_i\}_{i=1}^N$, factor endowments $\{\bar{L}_f\}_{f=1}^F$, and functional forms $\{F_i\}_{i=1}^N$, $\mathcal{D}$. ...

Wrapping up the last note of Feburary. The U of Chicago Symphony Orchestra performed Rachmaninoff Symphony No 2 today,The bundled piece is Too See the Sky, a contemporary violin concerto. Rachmaninoff’s Symphony No 2 touches on deep struggles of the composer’s inner thinking. I hear passages where the music suddenly swirls into dark passages, as if we enters through the composer’s unvisited despair. And, can modern composers stop bullying the piccolo? Giving piccolo exposed sixteenth scales in the lower register paired with impossible fingering — making the piccolo play everything that it’s not good at is not cool.

The academy nominated movie Tar (2022) tells a story of a fictional female conductor Lydia Tár. The film starts with Tár at the peak of her career (a protege of Leonard Bernstein, director of the Berlin Phil, finishing recording the last of her Mahler cycle). Some says the film is psychic, confusing, and somewhat creepy and haunting. Despite that Google autocompletes “What the heck is tar about” — the script is nice. Cate Blanchett did a great job. Music reference and orchestra scenes are highly accurate and professional. The movie’s deep. Reviews discuss a lot on Tár’s implied psychological nuance, feminism and anti-misogyny, as of the modern art world’s cancel culture, identity abuse (eg “I don’t like Bach because he’s straight and have 20 kids!”), and the long-argue about whether we should separate artist’s personal conduct with their work, etc etc. ...

Cross-country data reveals large total factor production (TFP) differences. This lecture asks: how does misallocation of inputs across heterogeneous firms affects measured sectoral TFP? Define and Measure Wedge in Monopolistic Competition Market Consider a market with final good producer, a continumm of intermediate good firms $i\in [0, 1]$, and households who supply labor inelastically at wage $W$ (taken as given by firms). The final good producer’s production function is $Y = \left( \int_0^1 Y_i^{\frac{\sigma-1}{\sigma}} \, \text{d}i \right)^{\frac{\sigma}{\sigma-1}}$. He maximizes $\max_{Y, \lbrace Y_i\rbrace}PY - \int P_i Y_i\,\text{di}$ take price as given. His maximization gives a demand function for each firm: ...

From the front line of macro class: Motivation Most poor countries remain heavily agricultural yet exhibit low agricultural productivity relative to richer economies. The central question is: what prevents reallocation of labor from agriculture to manufacturing? The class studied Russian industrialization (1880–1940) as a laboratory. Pre-1928: ~80% of labor in agriculture, yet agriculture accounts for less than 50% of value added. Post-1928 under Stalin: 30 percentage points of labor shift to manufacturing in 12 years, accompanied by famine and mass repression. ...

Sleepingbot has a bug that new blog posts in content/posts never made into the chatbot’s catalogue. Content never synced automatically. The original setup required manually running sync_content.sh after every new post. Really? So I scrapped the GitHub Actions approach entirely and replaced it with a local git pre-push hook (.git/hooks/pre-push): #!/bin/bash echo "Syncing blog content to chatbot..." rsync -av --delete content/posts/ blog-chatbot/content/posts/ git add blog-chatbot/content/ git diff --cached --quiet || git commit -m "Auto-sync blog content" echo "Sync done." This runs silently on every git push, syncs the posts, and commits them.

From the paper: Investigating the potential of investing in fine stringed instruments as an alternative investment asset. Angela ORTIZ MUÑOZ, 2020. PhD Thesis A helpful reference Investing in Fine Instruments, a PhD Thesis Investigation | Dr. Angela Ortiz Muñoz analyzes fine stringed instruments as an investment asset. Tarisio TL;DR: Violins are a real, if niche, alternative asset — not just a passion hobby. They offer ~3.5–5% real annual returns with genuinely low correlation to financial markets, meaning they earn their place in a diversified portfolio at a modest (~10%) allocation. The two most striking findings are that maker identity dominates everything else in price formation (Stradivari/del Gesù = +333% value impact), and that which musician plays an instrument functions like a credit rating upgrade, creating measurable abnormal returns. The market survived 2008 without a downturn, which is rare for any asset class. ...