I remember during the first Trump administration when he passed the tax cut and they knew it was going to widen the deficit, and people were like, 'Oh, where is the money going to come from?' And economics. That's called Ricardian equivalence. I think it's just a powerful argument. Right. You always need to think about where does the money come from, where does it go. And of course, in reality, all of this is violated. But as a first approximation, I think it's still useful to think about.
It doesn't work in reality, but it works. It works and it has robust explanatory power.
No, I've always found that to be very compelling. Perfect for a podcast and posting.
Hello and welcome to another episode of the Odd Lots podcast. I'm Tracy Alloway and I'm Joe Weisenthal. Joe, we're back in New York.
Yeah, but the Jackson Hole episodes continue.
That's true. You know what, by the way? Oh my gosh. The 30-year rate, by the way. Yeah. So it's just straight up the last few weeks, but it's right around as of the time that we're recording this. It's at 5.592. It has now hit the highest level since 2002. Wow. So, a few weeks ago, we were looking at the yield curve and take all these six. Oh, now, highest level since 2007. And then, like it was 2004 was the other year. And now we're getting higher since 2002. It really is both extraordinary speed and scale. Yeah. And the other thing that happened since Jackson Hole is we had a Fed rate hike. We had a Fed rate. And yet yields continue to soar. And so there's an open question over whether or not that rate hike is having its intended effect in terms of dampening down financial conditions. Yeah, things like that. And another big thing in the background, I feel like I have to list all these, all the big in the background. Yeah, yeah. But the other big thing is there's been this ongoing debate over Fed's communication style for a new communication framework. What does credibility mean when a central bank is in an inflation-fighting environment and things like that? We have to talk about all of this.
Yeah. It's when you said, another thing in the backdrop, there's another one. Which of these? I was like, which of the many things are you going to choose here? Because you could have said, oh, you know, this is against the backdrop of a large deficit, against the backdrop of AI spending and the fact that big hyperscalers are spending it and borrowing at rates that are like the size of, like pretty big nations, you could have said in the backdrop of long-standing questions about U.S. global hegemony and the link between war of the dollar and so forth, etc. And so there are many backdrops.
Yeah, infinite backdrop gone on for a while, but I chose not to know. There's so much going on at the moment. We know that all relates to rates. It all relates to rates. And we do, in fact, have the perfect guy, someone who can opine and share her research on many of these things. We're going to be speaking with Carolin Pflueger. She is an associate professor at the University of Chicago Harris School of Public Policy, as well as a visiting scholar over at the Chicago Fed. And she presented a very famous paper at Jackson Hole a couple of years ago. We didn't get a chance to talk to her when we were in Jackson Hole, but we're going to make it up right now. So, Carolin, thank you so much for coming on Odd Lots.
Thank you for having me. It's really great to be here.
Why don't you give us the sort of 3000-foot overview of your research. What's your specialty here?
So most broadly, I'm interested in how the macro economy, monetary policy and inflation are reflected in bond markets and are in turn shaped by them. I think that's a pretty interesting topic because we did to, you know, to me, the macro economy, inflation, unemployment, those are the big questions. They're not easy to answer about. That makes it all the more intriguing to me. And of course, financial markets are unique in that they're sophisticated. They're forward-looking. So a lot of these things get priced in. And that's what I find very interesting.
You said that you think about the way various things get reflected in the bond market, but then also how the bond market then reflects back on, to quote the real economy or the real world or whatever, that part doesn't get discussed as much. It's in its, it seems like for the most part, people, when they think about the link between the real economy and the bond market, it seems like they mostly make, you know, it goes in one direction. So it's interesting to me that you said reflected back.
Yeah. So that brings me to my research on the policy reaction function.
Now, what is a policy reaction function? Just as a quick definition.
Most simply, that's what markets or observers more broadly expect the Fed to do in response to economic conditions. So as a numerical example, if forecasters expect 2% inflation, they might forecast a 4% policy rate. If they expect 4% inflation, they might expect a 6% policy rate. So that would be a one-for-one adjustment, a perceived policy reaction coefficient of one. And that will, that matters. This policy reaction function matters for at least two reasons. And one of them comes back to your question. This reflection back, which is that if the policy reaction function is well understood, then that means that as data comes out, unemployment, inflation and so on, the policy rates will move in the direction that the Fed intended and thereby help with monetary policy transmission or maybe even speed it up before the next FOMC meeting.
So this is the market doing the Fed's work for it, correct?
Yes. But if you see only if the reaction function is understood.
That's, that's why the understanding is important.
Right. So if there is, you know, whatever the intended policy reaction function is, you know, if it's well understood, markets will adjust in response to inflation news coming out and unemployment news coming out and so on. How are you actually measuring how the market incorporates its future expectations of Fed policy? Because there are a number of ways that you could do it, you know, some obvious ones. But what are you looking at specifically?
Yes, that's a great question. So the, I mean, I think the main point is that a policy reaction function can and should be measured. And what my coauthors and I have done, I will think of is actually the most obvious way of doing it, which is, I think, always a good starting point. So this is joint work with my, is Michael Bauer at the San Francisco Fed and Addison Jerome at Harvard Business School. So we took two approaches. The first one is, based on forecasts, we didn't run our own surveys because we wanted to know, you know, how have these perceptions changed over time, historically, over decades? And, you know, a policy reaction function is quite similar to a 1993-type Taylor rule. But there are important differences. The first difference is that it's forward-looking. So it's about expectations. The second difference is that it's a little bit shorter-run. It's about the next couple of quarters at most. The next couple of years. And traditional monetary policy rules are used to describe historical data in a backward-looking manner. What we said is, hang on, rather than using this historical data, let's just use forecasts, survey data, interest rate forecasts, and it turns out that every month there's a lot of richness in terms of the forecasts that are being produced for the policy rate. And so we use this data from the Blue Chip of Financial Forecasters, which asks, what is your forecast for the Fed funds rate and what are, what are the underlying assumptions that you used for inflation and output to come up with this policy rate forecast? So it's in a way, it's an ideal setup because it's asking what is the role for the Fed that you're plugging in. And so we just took this, this data that's forward-looking, you know, 30 to 50 forecasters, a couple of forecast horizons up to six quarters out. And we ran the exact same regression that you would run for a monetary policy rule. But, you know, on this different data, forward-looking data. So yeah, if you basically treat this, we can't run experiments on the market economy. But if you treat this data as the best approximation of an experiment on the macro economy, you know, if we run a regression of the forecasted Fed funds rate onto forecasted inflation and forecasted output relative to potential, and so the coefficients there on inflation would tell us, when forecasters are forecasting high inflation, how much higher is the expected Fed funds rate. And vice versa, for example. So that's the first methodology. The second one is closer to what I said about the macroeconomic announcement dates and rates moving in response. That's of what you can use that also to obtain a market-perceived rule, just to say if inflation comes out higher or lower than expected. Let's say inflation comes out higher than expected. And interest rates, say the two-year rate goes up, goes up a lot. Then, you know, that is also a regression that we run. You need a couple of observations. Of course. It's a rolling window, but you can run this regression of how much do yields change onto what's the inflation news on that day. And that gives another way of getting a perceived reaction function. It turns out that both of these methodologies often give very similar answers. And I love that because it makes me a lot more comfortable with either methodology.
So one thing I'm curious about is, okay, you know, you look at market reactions and you get there's one number there, right? There's a lot of different views of the market, but it's one number. Whereas if you're doing a survey, you're going to get, you know, you can average them together, but you can see all the different views. Something I'm curious about with the surveys or anything is whether we see historical changes over time. With respect to, I suppose, the spread or the dispersion of views. Are there times where people in the market have a, you know, there's a clear consensus of what market participants or forecasters perceive the reaction function to be. And are there times that we could point to where there is a lot of ambiguity on the part of market participants?
Yes, that's, that's interesting. So we have focused on the variation over time and the point estimate of the reaction function.
There clearly is a lot of heterogeneity across forecasters. And we've done, we've looked, we've looked at that a little bit, but we've really focused on how does it vary over time.
But you know, as one example, when there was a lot of agreement, you know, after late 2011 was when the Fed came out and gave very clear date-based forward guidance, interest rates will be at zero at least until mid-2013. At that point, all the Fed funds rate forecasts collapsed to zero. And you know, you see absolutely no dispersion. So that would be an example where, where that collapsed.
