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What the Fed Knew, and When

Zachary BartschLeave a comment

I’ve recently gone back and started listening to the archived episodes of the ‘Macro Musings’ podcast hosted by David Beckworth. The show started in 2016. At that time, there was still a sense of malaise after the 2007-2008 Great Financial Crisis (GFC) and the slow recovery that followed it. We were also in a prolonged low-interest rate environment.

A recurring theme is whether the Fed should have engaged in expansionary policy earlier than they did in response to the GFC. There are multiple ways to answer. It’s not helpful to say ‘knowing what we know now’. The Fed didn’t have that opportunity. It’s a little bit more helpful to say ‘if the Fed had a different target or different tools’.  The target and tools are higher-order policy decisions and changing them can be helpful in the future. But they typically can’t be changed with the flip of a switch. After all, the 2% inflation target itself rolled out over the course of decades.

The most awkward/damning question is “Given the target, tools, and data that the fed actually had, did they make the right decision?”. If the answer is ‘no’, then that warrants a serious investigation of individuals, groups, processes, etc. I don’t mean a legal investigation. I mean the decentralized kind in which public and expert trust can be affected.

A concept that Beckworth often mentions concerning Fed culpability/performance during the GFC is the problem of data revisions. Currently, we know what the revised data says about NGDP, inflation, employment, etc. But the Fed only had the contemporary numbers and immediate revisions. In a world where economic growth is lousy or stellar in a range of 1-3%, small revisions can matter a lot. For example, below are the 2001q1 NGDP revision values over time.

Revisions occurred twice by 2002q2, revising NGDP down by more than 2%. Subsequent revisions raised the value on record to nearly +3% of the initial estimate, before settling at a less elevated value. Sheesh! In a world where a 1% swing is a big deal, how can we possibly expect the Fed to succeed at managing aggregate demand?

Things are not so scary as they might seem. The Fed doesn’t much care about revisions to an individual quarter. Rather, they care about the direction of change over time. Whether future revisions increase GDP by 2% is unimportant. What’s important is whether one period’s value is lower relative to the earlier value. That’s the relevant difference that tells us how the economy is changing.

Now, in 2026, our current understanding of NGDP during the GFC follows the below pattern starting in 2005q1 (lest I omit important pre-trends). NGDP growth had weakened in 2007q4, turning negative in 2008q1. Weak growth resumed in 2008q2. Then we had near-zero or negative growth for the next five quarters. Of course, we’re now approaching twenty years later, so we have the huge benefit of hindsight and revisions. Keep in mind that the contemporary numbers aren’t available until the subsequent quarter. By the yard stick of NGPD, the Fed should have been loosening by Q3 or certainly Q4 of 2008 if they cared about supporting total spending. Maybe as early as Q2 is they were especially sensitive.  

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The joy is in the noise

mdmakowskyLeave a comment

The Knicks won the NBA championship and the Canes won the Stanley Cup. The World Cup is here, with all of its grim authoritarian appeasement and absolutely incredible drama. It all serves as a reminder that so much of the joy is in the noise, the inability to forecast, both as individuals and collectively in the market, what will happen. Make no mistake, the hockey playoffs are grossly unfair as a measure of who exactly is the best hockey team (that was the Colorado Avalanche who were unceremonially swept in the conference finals). Pucks bounce, refs make mistakes, goalies get hot. Any knockout tournmanent is outrageously unfair to identify the best soccer/football team. Dominating teams losing 1-0 on a fluke goal is sufficiently frequent as to be commonly referred to as “getting footballed”.

And that, to be exceptionally clear, is the point. Sports remain an opportunity to watch something that is not only unscripted, but highly difficult to forecast with any sense of certainty. Upsets are joyful not just because they are unexpected, but because they happen just often enought that you don’t feel a fool hoping and cheering for one.

With all of the growing concern over sports gambling and prediction sites, I do wonder what people are more upset about. The self-debasement of individuals eroding their financial security in pursuit of a not-so-cheap high? Or the threat to unpredictability as the incentives of actors inside and outside the games being rearranged to undermine what is supposed to be a random number generator with a multi-agent human engine purposed towards creation of drama unpolluted by audience service and manipulation.

Because here’s the thing. People want to get hurt. They want the disappointmen of losing. Of failing. Or coming up just short. Of giving it away when victory was all but assured. They want that so that when things finally do work out they can wholly and earnestly give themselves away to celebration of something that really actually happened as a product of forces we cannot control. Movies, televisions, books, they all want to give you happy endings so you’ll come back. If they don’t they know what someone else will.

But sports? Sports cannot be bullied by the customer into any such contract because any competition there always and forever has to be a loser. And as a sports fan you will lose. Sometimes a lot. Sometimes for your entire life. But you keep coming back because the noise in the system says that you might win next time. You might get a lucky bounce. A hot goalie. Or a generationally great guard so grotesquely undervalued by another team that for the cost of having the 46th highest salary in the league, you get to have an NBA finals MVP lead you to your first championship in 53 years.

The joy is in the noise. Congrats to any and all Knick fans, especially my Aunt Jean, a dyed in the wool New Yorker who’s been waiting a long time for this.

Fast Food Was Solved Before Fast Fashion

Joy Buchanan1 Comment

Ozempic is not new anymore, and my friend is now losing weight on Wegovy. The miracle of science has been incredible to watch, because I know how difficult it was to lose weight by just trying to change habits before while the cravings persisted. (None of this is medical advice. These drugs are not for everyone.)

I have written about how the global apparel industry created something historically remarkable: a world in which almost everyone has abundant clothing in many styles. For most of human history, clothing was expensive and scarce. Today, Shein sells a dress for $8 and ships it to your door in a week.

The critics of fast fashion focus on the downsides: textile waste, carbon emissions, and low-wage labor. These concerns are prompting efforts to restrict or tax this abundance, as French lawmakers are now attempting with Shein.

Food abundance has some parallels with textile abundance. Once food became cheap, many people started complaining that we consume too much of it. We cannot seem to get it just right with consumption.

For most of human history, the primary nutritional problem was not having enough to eat. Famine was recurring and childhood hunger was normal. Innovation and globalization resulted in food abundance for most people. Caloric availability per person has risen dramatically.

The unintended consequence of that success is the fast-food era: a world in which cheap calories are engineered for palatability in ways that overwhelm the body’s natural satiety mechanisms. We solved scarcity, but now we have to deal with the abundance paradox.

These two abundance problems (clothes and calories) are at different stages of resolution.

The fast fashion critics are still waiting for their solution. The alternatives to disposable clothing seem to be either to get more expensive clothes or to change an entire culture (which rarely happens without violence). The political response of implementing bans or taxes amounts to restricting the abundance rather than addressing its side effects. Nobody has invented the equivalent of a metabolic thermostat for textile waste.

Fast food, by contrast, has just received something extraordinary: a drug that quietly recalibrates the neurochemical machinery driving overconsumption.

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Relative Measures of Portfolio Performance

Zachary BartschLeave a comment

This is the third and final installment of my series on portfolio performance measures among separate assets groups. First, I summarize the earlier posts, then I introduce relative performance measures. I start with the Markowitz cloud of possible portfolio weights, returns, and volatilities.

Absolute measures of performance contrast the realized portfolio performance with the performances that were possible simply by calculating the difference in, say, return or volatility. The drawback of this method is that different spreads of statistics can affect these differences apart from portfolio performance. That is, even if a portfolio of assets return was very high, some reference return can still be much higher and make the performance look poor.

Quasi-relative measures tackle this problem of different spreads by calculating the percentile of possible returns or volatilities. This allows us to compare portfolio returns to what was possible even among portfolios of different assets with Markowitz clouds of different volatility ranges. The drawback of quasi-relative measures is that the return at some percentile of possible returns is not the same as the return of the same percentile among possible portfolios. Said another way, each possible rate of return in the Markowitz cloud is not equally as likely. So, a low percentile among possible returns be due to a very high and unlikely return.

It should be obvious that returns and volatilities among possible portfolio weights are not equally likely. To help visualize the idea, see the below 3D quadratic for a simplified example that represents a portfolio of three assets. The x-axis represents returns and the z-axis represents standard deviation. The y-axis represents the weight on  the 3rd assets (returns and weights map directly to one another linearly). The set of possible portfolios lie on the surface of the quadratic function.

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A thought on the SpaceX IPO

mdmakowsky1 Comment

The SpaceX IPO is set for June 12th, with an anticipated market cap after day one between $1.5 and $2.5 trillion. Most of that valuation is based on the prospect of dominating the market for satellites, putting data centers in space, and the endless demand for computing power from AI. It is essentially an AI-related market power play.

I have no speculative insight into the value of SpaceX stock as an investment, but I am an inveterate, unrepentant consumer of irony. An IPO is a speculative investment, but it’s also the act of becoming a publicly held company. A large part of being a public company is getting the accounting right. Modern accounting has all kinds of informational value, but from the point of view of large companies it’s mostly about minimimizing taxes while maximizing perceived value. Both of those ambitions include strong incentives for malfeasance, which is why we have audits, financial regulation, and the IRS. The IRS and financial regulation have been defanged, however, mostly due to a lack of personnel from aggressive destaffing, at least some of which you can lay at the feet of DOGE. You can’t audit a massive company effectively without accountants.

Or can you?

I can’t think of a technical task that is more perfectly suited to AI than auditing a public company’s accounts and SEC filings. You feed AI a billion previous filings, all of the associated laws and regulations, and then flag all the records previously found in violation. Then you feed it new ones and say “show me the violations and discrepancies in rank order of dollar value.” A hundred good accountants using a dedicated AI, that’s exactly the kind of story that leads to the order of magnitude increase in labor output that the biggest proponents of AI are looking for.

Never forget that the event that initially popped the dotcom bubble was Microstrategy getting caught cooking the books.

I know you can’t write history like a novel, but “IRS, previously destaffed by Musk-headed DOGE, is forced to use AI enabled audits and finds massive revenue discrepancies, leading to panicked sell-off of Musk-headed IPO record holding company and kicking off AI stock sell-off”…that’s too easy, right?

Would you steal a lemon?

Joy BuchananLeave a comment

My latest at EconLog is

Would Hasan Piker Steal A Car?

Click the link above to read but here are some quotes:

Hasan Piker implied that he might steal a car if it carried no consequences. In the interview, author Jia
Tolentino also casually admits to shoplifting lemons from Whole Foods. Although petty theft is common, the interview clip spread quickly …

In my recent paper with Bart Wilson, “You Wouldn’t Steal a Car: Moral Intuition for Intellectual Property,” we test how people think about taking different types of goods.

What would Piker think of a world where the “microlooting” he claims to approve happens at scale?

Tall poppies don’t get the calls

mdmakowsky1 Comment

Ask anyone who grew up playing basketball as the tallest player on the court and they will, each and every one of them, tell you that players were allowed to foul them harder and more often. If you were tall you didn’t get the calls, full stop. Why? We could sort through a host of mechanisms, but they all boil down to “Being tall is an unfair advantage. It’s only fair that I, the shorter opposing player, am allow to slap you, chop you, kick you, trip you, grab you.” To be honest, I don’t think this is a particularly shocking phenomenon. “Tall poppies get cut down” is a cultural cliche for a reason. What is interesting is that it persists even amidst billions of dollars in market incentives pushing in the other direction.

The latest version is happening right now as the Oklahoma City Thunder are currently doing their best to end Victor Wembanyama’s nascent career each and every night, and the referees seem uninterested in realigning the incentives otherwise. At the moment the Spurs are currently up 65-43 in game 4 of the series. If the series goes 7, there’s at least a 20% change Wembanyama doesn’t make it to the end. Will they break his foot smashing down on it, break his leg tripping him, or dislocate his shoulder yanking down on it from a leveraged position? Don’t know, but they’re doing their best to make it happen.

Caitlin Clark came into the WNBA as the single greatest talent prospect in the history of women’s basketball. The abuse she suffers is well documented. Wayne Gretzky was the greatest hockey player of all time, but he was arguably only allowed to reach his potential because Bobby Orr’s careers was cut in half by a league that allowed teams to abuse him with little to know punishment. Bobby Orr’s sin was that he was such a better skater than everyone else that, if allowed to play without constant grabbing, hooking, and abuse tantamount to aggravated assault, he would have walked away with too many goals, wins, and Stanley Cups. It wasn’t fair that he was so much better, so they let the players even the odds. Having watched him limp away after only 7.5 seasons, the NHL took the unofficial position that Gretzky’s teammates (specifically, Dave Semenko and Marty McSorley) held carte blanche to assault anyone who touched Gretzkey. While perhaps not a culture-shifting solution, Gretzky did have a 20 year career that brought hockey to new heights of popularity, so it was ostensibly effective.

But none of that gets at the underlying economics. Elite players bring big audiences to sporting events, which in turn, brings in big money for everyone. The owners, players, and everyone in between gets richer when elite players shine under the biggest lights. So why chop them down? Well, first we have a collective action problem to solve, because, yes, the entire market benefits from superstars, but their opposition during the course of play in any one game have the individual incentives to do whatever they can get away with to win. That’s why we have referees, commissioners, and a players union: to solve those collective action problems. All of those rules and institutions are in place specifically to align incentives and bargain for outcomes that maximize welfare. So why aren’t they working?

When you find cliches at the front of your mind, decent chance you’re running up against psychology and behavioral economics. And as Victor Wembanyama is learning each and every night of the playoffs, “tall poppies get cut down”. It’s not fair that he’s the first 7’5″ player with elite NBA level skills to ever play the game. You know, I was never a fan of watching Shaq play basketball per se, but I always knew he should have scored at least 40 points every night. Yes, he committed 7 offensive fouls every game, but he also received 25 fouls that went uncalled. Players were allowed to maul him because it was unfair he was so much bigger, stronger, and more athletic. His career was only as long as it was because his body could endure the abuse. There has never been another player in NBA history who could have survived even 3 seasons receiving the abuse he did.

Putting aside simple behavioral explanations, we also should consider the possibility that NBA team owners and players are so far down the diminishing marginal returns to wealth, that the median participant would actually prefer to earn less money in order to maximize their own chance at winning a championship. They want parity, of a sort. Parity, but only once the playoffs arrive. The regular season is too long and everyone does, in fact, want to make money, so the abuse is minimal, but once the playoffs arrive, the collective preference is for parity delivered via weaker rule enforcement. There are only so many elite players, but everyone is capable of low-level violence. This preference for postseason parity may also explain why Oklahoma City’s best player, Shai Gilgeous-Alexander, has the reputation for simulating being fouled on every play. If you’re going to get fouled no matter what, you might was well maximize the probability of getting a foul call by forcing the referee to be observed observing the incident.

And, to be clear, parity may in fact be revenue maximizing. Just look at the NFL – the entire structure is designed to maximize the number of franchises who believe at the beginning of the season that their team has a chance to win it all. The players are relatively anonymous compared to NBA superstars, but fans are mostly there to root for laundry, and in the NFL, so long as that laundry doesn’t say NY Jets on it, there’s at least a glimmer of hope. Counter point, just look at the NFL. They understood that each team, especially once the playoffs started, had strong incentives to try to end the opposing quarterbacks career on each and every play. So the NFL introduced a battery of rules to protect quarterbacks, and it seems to have worked.

So maybe I’ve come full circle. Maybe this is what the NBA wants. But I really, really it’s hope not. Wemby is special. I’d like to see the very most of what he can become.

Breakfast Pudding

Zachary BartschLeave a comment

When I was a kid, my family didn’t get JELLO puddings – or any puddings for that matter. As an adult, I realized that a lot of those are just sugar, cornstarch, and stabilizers. So, they became less appetizing.

You’re going to laugh at me.

A few years ago my wife and I went to a nice little breakfast restaurant for brunch in old town Fredericksburg, VA. I got this coconut milk chia seed parfait. I was blown away. That seems silly to say, but it was really nice.

For years we spoke longingly of that chia parfait and we’d speculate about when we might go there again. It was one of those conversations that married people have.

“Hey, remember that really good thing?”

“Yeah, it was really good.”

“We should try that again sometime.”

Then one day, while visiting Virginia, we noticed that the restaurant had closed. It wasn’t surprising because the restaurant had only been ‘fine’, except for the healthy and delectable layered treat from years past.

Now we have a handful of kids and we try different things periodically to make the morning routines go more smoothly. Having a responsible treat to entice juveniles from their room isn’t the worst thing that we’ve tried.

My wife, in her laudable creativity, refined a new creation that’s inspired by our now frustrated longing for a nice chia parfait.

Below is a recipe for peanut butter chocolate chia seed pudding. Basically, you mix it the night before and stir it again in the morning and it’s ready to go. It’s a crowd pleaser.

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Economic history as it’s happening is alway relative

mdmakowskyLeave a comment

This is the chart that I’ve been thinking about today.

The US government has been able to borrow on the cheap for most of it’s existence, with the exception of 70s and 80s when stagflation put the clamp down. Treasury rates are soaring right now…or at least, it feels that way because for most of my adult life the United States has been viewed as arguably the safest borrower in history. What follows are in some ways the only two questions that matter for the US economy. Is the US government a reliable institution? Is economic growth going to keep pace with inflation? The answer to each question (and their subcomponents) is, of course, unknown, but the market seems to think the net of that question is going in the wrong direction.

That said, for all of the neverending parade of (sometimes unintential) nostalgia that seems to pollute the discourse, wow, 1975-1985 was not exactly macroeconomically “aspirational”.

Will AI kill the research paper?

mdmakowsky3 Comments

Will AI kill the research paper?. I don’t know, probably not. But I do know that what has constituted a research paper has changed many times before and will change many times again.

Before the the 1940’s, economics research papers were largely prose. Analytic in nature, sure, but prose. Some graphs, maybe a box. A little math, but math largely for the sake of demonstrating logical relationships. Then Samuelson hit, reframed economics as thermodynamics and differential calculus. What was previously a research paper was was now a polemic, a monograph at best. Thought experiments were out, high theory was in.

This era of high theory flourished in the 70s, the math changed, and at some point computers arrived with the possibility of data sufficiently rich and numerous you couldn’t just plot all of the observations in Figure 1. That data couldn’t stand on its own, though. To be a credible publication you really needed to bundle your analysis with some theory that generated testable predictions. Pure theory papers gave way to an era of applied imperialism as economic models found themselves applied to every quantified context under the social scientific sun.

Causal identification became a thing of interest, and we got really good at telling stories again. Specifically, stories about instrumental variables. You needed a story to convince anyone, but we told so many that some folks started to notice that these stories were often pretty weak. That, in part, turned up the heat on a credibility revolution that was already in swing, which meant now you needed even better data and you needed to defend you identification strategy to the death. What was a paper before was now an embarassment you should probably consider retracting (nb: no one retracted anything, but that doesn’t mean people were suggesting it behind their backs).

Which kept rolling in data set after data set until we woke up one day and realized you either need to go out in the world and create your own actual experiment (nothing quasi- about it) or you needed to cultivate access to better…no, better…no, the very best-est, most detailed and granular administrative data ever, preferably a universe if possible. Data so perfect as to allow for contributions unassailable in their legitimacy. Do you have friends at the Danish Census? If you want tenure you should probably start flirting with someone at the Danish Census.

So a paper was a paper. Until it wasn’t a paper anymore. Until that wasn’t a paper anymore. Until that wasn’t a paper. The Recursive Dundee Theory of Research*, if you will. They all met the criteria of a contribution, until they didn’t.

So what does this mean for AI and research papers now? Well, if we look to thermodynamics in the 40s and cheap computing power in the 90’s for analogues, then I’d say it’s going to reshape the criteria for a contribution in no small part because it lowers the cost of mediocrity. Mediocre analysis will no doubt persist, but it will shift over into blog posts and journals no one ackowledges as legitimate. Do remember, please, that mediocrity is a relative concept. The quality of blog posts and publications in scam journals will likely massively improve as what can be accomplished in an afternoon’s work is radically increased. Don’t worry, I have no intention of improving beyond my current warm bath of blogging unremarkableness, but others will likely cave in to the pressure.

What about the papers in top journals, though? The papers Tyler is presumably talking about. Will AI kill those economic research papers? Probably not, but it will likely improve it significantly. Why? For the same reason that Michael Kremer says that technology and quality of life improve with the size of the human population. More people means more ideas, and there is nothing more important to economic growth than the sheer number of ideas. And no, I do not mean ideas generated by AI’s. I mean the raw number of researchers with the capacity to make major contributions is increasing dramatically because we’re all getting research assistants. We’re all getting copy editors. We’re all getting support. That’s how AI is going to change the research paper: by giving more ideas the support they need to reach the light of publication. The bar is going to get higher for the same reason that the level of sports improve as you widen the geography they pull from. There’s someone at a directional state school who didn’t get the placement they deserved out of grad school. Sure they have to teach a 3-3 load, but they’re licking their chops right now because they don’t need an army of grad assistants. Summer is here and they’ve got everything they need to make a contribution.

Or I don’t know. Maybe AI will do all of our thinking in 50 years. Forecasting technology beyond 5 years is like forecasting weather beyond 5 days: I can’t do it and neither can you.**

*Apologies to Justin Wolfers and all my Aussie friends for a bit of cultural appropriation. I promise to put some Vegemite on toast while enjoying a flat white and explaining Aussie Rules Football to a friend within 90 days.

**Except for Neal Stephenson. That guy’s the Warren Buffet of Sci Fi forecasting. Maybe he’s the one in a billion person actually experiencing one in a billion level luck, but that doesn’t make it any less impressive.