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Can central bankers become Superforecasters?

Can central bankers become Superforecasters?
Aakash Mankodi and Tim Pike
Bank Underground, March 12, 2018






Tetlock and Gardner’s acclaimed work on Superforecasting provides a compelling case for seeing forecasting as a skill that can be improved, and one that is related to the behavioural traits of the forecaster. These so-called Superforecasters have in recent years been pitted against experts ranging from U.S intelligence analysts to participants in the World Economic Forum, and have performed on par or better by accurately predicting the outcomes of a broad range of questions. Sounds like music to a central banker’s ears? In this post, we examine the traits of these individuals, compare them with economic forecasting and draw some related lessons. We conclude that considering the principles and applications of Superforecasting can enhance the work of central bank forecasting.

Setting the scene

It is helpful to begin by considering the purpose of forecasting in central banks, and how the process works in practice.  This speech by Gertjan Vlieghe explains how forecasting is an important tool that helps policymakers diagnose the state and outlook for the economy, and in turn assess – and communicate – the implications for current and future policy. So achieving accuracy is not always the sole aim of the forecast. However, forecasts are also a means to provide public accountability of central bank actionsand the presence of persistent or significant forecast errors may damage the credibility of the policy making institution amongst key stakeholders (individuals, governments and financial and capital markets).

A typical forecast set-up at a central bank is (see here) supported by two pillars: i) statistical frameworks underpinned by specific (for example, New Keynesian General Equilibrium) economic concepts, which can be supported by tools that process a range of economic and financial data, and ii) monetary policymakers’ judgements and deliberations that overlay these strict model-based forecasts – all of which form part of the deliberation process.

The accuracy of such forecasts has come under much scrutiny (see here) since the financial crisis, resulting in a great deal of effort to improve their performance. Several reviews and studies (see Stockton (2012)BoE IEO (2015)FRBNY Staff Report (2014) and ECB WP 1635 (2014)) have evaluated forecast performance across many major central banks and suggested improvements in calibrating economic models (e.g. to reduce bias), challenging prior conventions more and learning more from other central banks/economic forecasters.  The BoE’s MPC for example has also started commenting on its own ‘key judgements’ in its quarterly Inflation Report.

This is all welcome progress. But this iterative process from inside the central banking community over time leaves us with an impression that improving forecast performance could benefit from further considering the successes of forecasting  in other fields (similar to taking an “outside view” when forecasting as described by Kahneman). We may then move forward from this process of gradual evolution… to a potential revolution.


Superforecasters have been described as “unusually thoughtful humans on a wide spectrum of problems”. They are drawn from necessarily diverse backgrounds, and include amateurs and experts in a given field. They compete in tournaments which test their judgements on a range of questions about economic or geopolitical events. And through making these predictions they are expected to hone a range of forecasting skills. They are judged on several measures (including a daily average ‘Brier score’ – a measure of forecast accuracy originally proposed to test weather forecasting), and they receive their title by consistently outperforming a top percentile of their peers.

Superforecasters were first identified on the back of The Good Judgement Project (now a private enterprise)which was part of a US Intelligence Agency program in 2011. The GJP’s testing team included renowned advisors from psychology, statistics and economics. Their work used personality trait tests and training methods to reduce cognitive biases and improve the forecasting abilities of their volunteer forecasters. They then identified individuals who consistently out-performed their peers. Subsequent studies of this experiment found that when these top forecasters were placed in teams with other such forecasters  (described here as ‘group of average citizens doing Google searches in their suburban town homes’), they performed around 30% better than the average for intelligence community analysts who had access to confidential intercepts and other relevant data. Pretty Super-ising results one might say!

Can central banks become this ‘super’?

The story so far could imply that the answer simply lies in replacing central bank forecasters with these Superforecasters and leaving them to it. However, central bank forecasting is as much about forming a coherent economic narrative (the preserve of economists) as it is about numerical accuracy (for which the traits that make these individuals outperform the ‘experts’ matter). Central bankers may have a comparative advantage in the former, but their forecasting can be enhanced by considering key behavioural traits of those responsible for forecasting.

So how do central bankers fare against these Superforecasters?

The similarities: Superforecasters (most importantly) have a ‘growth mind-set’, which is a real willingness to address why a forecast is different from its eventual outcome, rather than just an ex-post evaluation of whether the prediction was correct. They also demonstrate a good balance of data and judgements when forming conclusions, not placing undue weight on either one.

Central bankers in comparison likely fare favourably against these traits, given most major central banks provide detailed updated assessments (the ‘why’) accompanying changes to their forecasts on a regular (usually quarterly) basis. At the BoE, these follow a substantial consultation process between staff and policymakers.

Some differences:

Using the wisdom of crowds: Another key trait of Superforecasters is that their forecasting abilities are enhanced when working in diverse teams – with people drawn from a range of disciplines, levels and areas of expertise. This enables them to tap into the well-known concept of the wisdom of crowds, and the process reportedly leads to better forecasts by providing a more stimulating environment for debate. Results are further aggregated to give more weight to forecasters who have a better track-record.

The central bank forecasting process does incorporate some elements of this – for example, many central bank policymakers make decisions in committees, after debate and exchanging views.  Moreover, several central banks regularly use surveys of external economic forecasters as an input to the forecasting process, or draw on external views, e.g. the use of the Agency or Market Intelligence networks to gather views in the BoE.

But (we would assert that) the forecasting outputs do not benefit in the same way as the Superforecasting process, where particular behavioural traits, a mix of expert/non-expert opinions or previous track records of those forecasters are considered.  Engaging a wider cohort of participants in forecasting could address this.  One suggestion would be to createCitizen Economists – as suggested by Andy Haldane in this speech, who argued that the wisdom of crowds can be harnessed by regularly canvassing the views of the public on the economy. Central banks could consider creating an online platform that engages the public directly with forecasting – which might also improve public understanding of policy and the economy (the RSA’s Citizen Economic Council and the Bank’s recently announced Citizen Panels for example intend to achieve a similar purpose). Central bankers can use these as an input into their own forecast process (perhaps even publishing the alternative crowd sourced forecast, similar to the way that the Fed publishes a staff forecast alongside the FOMC’s official one), though policymakers would remain accountable for their own forecasts and any policy decisions based on them.

Competing forecasts: A further avenue to explore is whether central banks might engageexternal Superforecasters (who aren’t constrained by the same institutional challenges as central banks) to produce their own macro-economic forecasts. Superforecasters currently partner with organisations (e.g. humanitarian or policy-making) around the world on topics ranging from geopolitics, future currency movements, and economics. In a similar vein, central banks could use Superforecasters’ macro-economic forecasts alongside their surveys of external economic forecasters as an additional input to the forecasting process.

Central Bank Superforecasters: Central banks could also try to identify and train their owninternal economic Superforecastersby employing the same techniques of cognitive training, team-work and result aggregation as another additional input to the forecasting process. One part of such a programme could be the continual assessment of forecasting performance, as measured by Brier scores.


With continuous forecasting challenges on the horizon in coming years, perhaps it is an opportune time to incorporate these ideas in the central banking sphere. Economic forecasting will always be an imperfect science. So while it is unlikely that a major shock such as the global financial crisis would have been averted by improving the accuracy of forecasting efforts in these ways, we believe the lessons learnt through the experiment of Superforecasting have a lot to offer to take forecasting a step forward in that direction.  Potentially over time, we might be able to create a next generation of central bank Superforecasters.

Aakash Mankodi works in the Bank’s Market Intelligence and Analysis Division and Tim Pike works in the Bank’s Agencies Division.

Transcript: Alliance Bernstein’s Kathleen Fisher



The transcript from this week’s MIB: Kathleen Fisher, Alliance Bernstein is below.

You can stream/download the full conversation, including the podcast extras on iTunesBloombergOvercast, and Soundcloud. Our earlier podcasts can all be found on iTunesSoundcloudOvercast and Bloomberg.


This is Masters in Business with Barry Ritholtz on Bloomberg Radio.

BARRY RITHOLTZ, HOST, MASTERS IN BU.S.INESS:  This week on the podcast, we have a special guest, her name is Kathleen Fisher, she is the head of wealth and investment strategies at AllianceBernstein.  Bernstein manages about $540 billion, they’re just a giant company in 22 countries, 3,500 employees, I think that something like 200 analysts and 150 portfolio managers, they are just a behemoth and she runs all of wealth and investment strategies.

If you’re at all interested in asset management and what it’s like working at a giant firm or what it’s like to be a woman at a very senior level and really Wall Street still is a male-dominated profession, this is really an interesting conversation.

There are a few people who know this business and know the human side of it as well as Kathy, she’s just tremendously knowledgeable and insightful and full of all sorts of intelligent commentary.

I think this is the sort of conversation so if you’re at all interested in asset management, wealth strategies, investment portfolio strategies, this is the podcast for you.  With no further ado, here is my conversation with AllianceBernstein’s Kathleen Fisher.

My guest this week is Kathleen Fisher, she is the head of wealth and investment strategies at AllianceBernstein, a firm that manages about $540 billion with 3,500 employees in 22 countries.  She joined the firm as a senior portfolio manager and member of Bernstein’s private client investment policy group.  Before joining Bernstein, she spent 15 years at JPMorgan, most recently as managing director advising banks on acquisitions, divestitures, and financing techniques.  She graduated from Bates College and has an MBA from NYU.

Kathleen Fisher, welcome to Bloomberg.


RITHOLTZ:  And I’m going to call you Kathy instead of Kathleen.

FISHER:  Yes, thank you.

RITHOLTZ:  I know that is your  to the article you Kathy Kathleen.  I know that is your everyday name.  You  started as an equity analyst, what attracted you to finance in the first place?

FISHER:  Let me back up say I started as a young economist, I started at the Federal Reserve Bank of New York, I was there for three years, I did monetary research as well as GDP research.  It was an amazingly wonderful learning experience to do high quality  research.  It was very academic but I learned the importance of getting your facts right which today is more relevant than ever, isn’t it, when we have a word that’s called fake news.

RITHOLTZ:  Fake news.

FISHER:  But it was a great learning experience about the importance of footnotes, about citing your sources that has followed me in everything I have done since then.

Having come to New York to do that job, I did start meeting people who were in investment banking and equity research and lo and behold, someone I met gave me an opportunity to go to Morgan Stanley to be a bank stock analyst and that’s how I got into equity research which back then, it was still a relatively new field, very exciting, and for me, very intellectually rewarding to focus on relative valuations but to also learn that what you think is so right often takes the market much longer to agree with or perhaps never agree with.

So it’s a great learning experience.

RITHOLTZ:  Any particular example stand out?

FISHER:  Well.

RITHOLTZ:  This was quite the memorable experience.

FISHER:  I was covering bank stocks at a time when bank stocks were very much out of favor, and therefore no one wanted to hear a word I said for quite some time.  So let’s just say that in it of itself was a great experience.

RITHOLTZ:  So you spent a lot of your career at JPMorgan after Morgan Stanley, what was that experience like?  What did you do for them?

FISHER:  My turning it JPMorgan was incredibly fortuitous because I did a lot of bank M&A at a time when bank M&A was absolutely a huge trend in the late 80s, early 90s, mid-90s, so it was a very active space and tons of activity so it was really — a wonderful experience and one captured both all that one does in M&A and corporate finance valuations and the right price to pay for a deal.

But also it brought in the human side of things because the bank mergers, needless to say, cost reduction and people reduction, and you had to get your head around that and the important thing is in a world that makes sense over time, it didn’t  makes sense for the banking industry to shrink.  Those job cuts would come eventually, those mergers accelerated them but it was something that you have to stand back and so now what’s going to happen over time regardless of when.

RITHOLTZ:  So your tenure at JPMorgan, I believe, that predated the Jamie Dimon era, is that correct?

FISHER:  Very much predated, it was pre Chase merger, actually.

RITHOLTZ:  Oh, okay.


RITHOLTZ:  So I was going to ask you how much time you spent working with Jamie, but obviously…

FISHER:  Quite a long time ago.

RITHOLTZ:  Obviously not.

So what led you to AllianceBernstein although if memory serves, when you joined, it was Bernstein still, right?

FISHER:  Now I joined in 2001 and the merger occurred in 2000.  And so I came post the AllianceBernstein merger, I joined AllianceBernstein because several of my colleagues in JPMorgan had migrated to AllianceBernstein over time and therefore they enticed me to join the firm.

RITHOLTZ:  And you’ve been there ever since?

FISHER:  I have been there ever since 2001?

RITHOLTZ:  So your current title is head of wealth and investment strategies, what does the head of wealth and investment strategies do?

FISHER:  Well I am blessed to work with a team of 35 extraordinary experienced professionals who are in two separate groups.  One is the Wealth Strategies Group which developed the models that help our clients preexperience the likely financial outcomes of decisions they can make.  Whether those decisions are around wealth transfer strategies or retirement or charitable giving, the idea that you have many variables you can control and therefore how to think about different asset allocations, different structures, we help our clients think through all those options.


Transcript: Jerome Schneider, PIMCO



The transcript from this week’s MiB: PIMCO’s Jerome Schneider  is below.  

You can stream/download the full conversation, including the podcast extras on BloombergiTunesOvercast, and Soundcloud. Our earlier podcasts can all be found on iTunesSoundcloudOvercast and Bloomberg.


ANNOUNCER: This is Masters in Business with Barry Ritholtz on Bloomberg Radio.

BARRY RITHOLTZ, HOST: This weekend on the podcast, I have an extra special guest. His name is Jerome Schneider. He is the head of had a short-term portfolio management of PIMCO. If you are remotely interested in fixed income bonds, trading, the plumbing of how finance works, this is a master class and tremendous details of how the fixed income market works. It is absolutely fascinating.

If you are remotely considering any sort of fixed income investing, working on a bond desk, being a portfolio manager of any sort, then this is a conversation you have to listen to. It’s absolutely fascinating.

With no further ado, my conversation with PIMCO’s Jerome Schneider.

My special guest today is Jerome Schneider. He is the head of short-term portfolio management and funding at PIMCO which manages about $1.75 trillion as of the end of 2017.

Prior to joining PIMCO in 2008, he was a senior managing director at Bear Stearns specializing in credit and mortgage related funding transactions. He held a number of various positions on the municipal and fixed-income trading desks at Bear. Morning Star named him the Fixed Income Fund Manager of the Year for 2015. He manages three separate funds, one over $14 billion dollars, the other, over $8 billion, the smallest, a mere $2.2 billion. Jerome Schneider, welcome to Bloomberg.


RITHOLTZ: This is the perfect time to be speaking with you given everything that’s going on in — with the Fed, with rising rates, with yield curve, but let me start with a little bit of background. How did you first get interested in finance?

SCHNEIDER: Pretty easily on, I had a great uncle who was always sort of fascinated with the stock market at that point in time and had bought me a handful of shares, you know, like everybody does and from that fascination, you quickly realize that the power of capital and I think at the age of 11, I had asked my dad, you know this stock market thing is pretty interesting.

Let’s read about it. Let’s read about it on the Wall Street Journal, and for my 12th birthday, he actually took me to the Stock Exchange on the floor and for young chap from Oklahoma, Oklahoma City that is — that’s a pretty, pretty empowering thing to see your dream location come true.

So, for me it was a trip to the Stock Exchange and to see the Yankees who I loved at that point in time and really, put together in your mind how you actually get to that point from being 12 to being a young professional and the steps it takes, so that was a magical moment in my formative years.

RITHOLTZ: And that was back in the day when you could both A, get on the floor of the Stock Exchange, you can’t do that really today and B, it’s not just the front (ph) for a television studio, that was where stocks were actually traded back then.

SCHNEIDER: Yes, and amazing and thinking about it, you know, I was probably hardly five-feet tall at that point in time. You know, it was a scrum and this was in the mid-early 80s and I looked back at the photos we took and the funniest thing, obviously is the people and how they are dressed and second of all, it was a functioning entity in a physical sense, not just a literal sense and a spiritual sense as it is now, along with computers, but it’s a physical breathing entity.

And then today, obviously, it’s changed and the NYSE is — and all the stock exchanges have their functioning perspective to code up to technology, but I think more importantly, and this is the thing that I would say is that, as a young person having the ability to have that experience and learning from people what it took — it takes to get there and then putting those steppingstones in place, seeing the right people, understanding what they took to get there even though they might be 10, 20, 30, 40 years your senior — that’s a very powerful thing.

And I think, one of the key things for people whether they are interested in finance or otherwise is to find people that will serve as mentors, rabbis — whatever it is to help empower them to achieve their goals in that kind of way and I was just fortunate to have a ton of people around me.

RITHOLTZ: It sounds like that was a formative experience for you.

SCHNEIDER: Yes, it was. It was great and I think, you know, people recognized that at that point in time, as odd as it might be from young kid in Oklahoma City, it might have been one of those things that it was a way out, so to speak, and so for me Oklahoma is a great place to be from and is a great place to be going back to with family, but at the same time, I haven’t lived there since high school.

RITHOLTZ: No interest in being a roughneck and working in the oil fields or any of that?


RITHOLTZ: Physical labor?

SCHNEIDER: Not at this point. Well, I mean, that was the other formative expense in my life actually being exposed to the roughnecks and when you grow up in Oklahoma and Texas and your whole family is exposed to the oil industry, in the late 70s and early 80s —

RITHOLTZ: During the oil crash? Yes, sure.

SCHNEIDER: The oil bust basically was an eye-opening experience and then frankly, that was one of the things that led me to want to understand capital markets because, you know when you’re in the oil business, you’re putting together a ton of capital, a lot of it is not your money and so your incentives are very different.

And at the same time, when you think about the ramifications of a re-pricing event, in that case, it’s oil and everybody — I mean, they’re in there sitting on the oil patch and things, oil prices only go, up but as a young kid, you see everybody going from having literally Learjets and third and fourth lake homes and multiple cars to nothing overnight and you look around, and you know, we had a very modest upbringing.

I would say that, you know, in retrospect, it was – the (ph) down side is fairly limited compared to some people —

RITHOLTZ: Not a lot of leverage —

SCHNEIDER: Well, not a lot of leverage, so to speak, but at the same time, not a lot of the different up side, but I learned at that point in time, the strength of leverage and the danger of leverage, which oddly, as my professional career evolved in the fixed income — that obviously became a keystone to that.

RITHOLTZ: So, you go to University of Pennsylvania and then you get your MBA at NYU Stern?


RITHOLTZ: And what was your first job right out of the school?

SCHNEIDER: So, when I graduated Penn, I wanted sort of a degree that was related to finance, but really more economics related and so I had a more customized degree in international finance economics and international relations and so, Penn was a perfect place to do that.

Unfortunately, when I was graduating and started with an interview in 1994, my background was a series of internships for a small — from a small shop in Oklahoma City called Stifel Nicolaus —

RITHOLTZ: Oh sure.

SCHNEIDER: And then —

RITHOLTZ: Which is now not such a small shop.

SCHNEIDER: Not such a small shop, but they were really focused on muni bonds back then, which is a good and bad thing and the other one was running a guy’s campaign for state treasurer of Oklahoma, which was successful, but that was both — it took me back to Oklahoma and so, as a result, at Penn, you’re looking out for internships and most of the kids in the East Coast had connections to New York and Wall Street and things like that and I didn’t have any of those connections, so to speak.

So, I was really trying to find my way to get to Wall Street at that point in time and it took a little bit more effort. That combined with the fact that when I was graduating in 1994, it wasn’t the best job market in the in the world and when you think about it, you had to get in on any floor whatsoever. So, I interviewed. I interviewed with people who were trying to sell limited partnerships, limited — people who were trying to trade stocks and be in the operations group, and oddly, coincidentally, the job I took was with Bear Stearns and I joined their operations training program at that that tender young age for a very small salary, but a great opportunity to learn.


Transcript: MiB with Tom Gilovich of Cornell University



The transcript from this week’s MiB podcast with Tom Gilovich is below.

You can stream/download the full conversation, including the podcast extras, on BloombergiTunesOvercast, and Soundcloud. Our earlier podcasts can all be found on iTunesSoundcloudOvercast and Bloomberg.



ANNOUNCER: This is Masters in Business with Barry Ritholtz on Bloomberg Radio.

BARRY RITHOLTZ, HOST: This week on the podcast, I have an extra special guest. His name is Tom Gilovich, he is a professor of psychology at Cornell University and I have to say he is a person who has had over the years, tremendous influence on my career although admittedly unknowingly, I began in this industry as a trader and I was frequently perplexed and fascinated by why the people on the desk around me were doing either poorly or well any given day, week, month, and I eventually figured out it wasn’t that one guy was smarter than another or someone had more knowledge, it was their own behavior that led to their success or failures. And so I started hunting for some information about why people made certain behavioral decisions that they did.

I ended up tracking down a book of his from 1991, “How We Know What Isn’t So” and that pretty much sent me down the rabbit hole of behavioral economics which has been a tremendous asset to me both in the world of finance and media. Understanding what motivates people to make either good or bad decisions with their money is a tremendous asset both what you should be doing with your money and what you should be advising other people to do with their money.

This is, to me, one of the more fascinating conversations that you’ll hear if you are at all interested in fill in the blank, psychology, behavioral economics, heuristics, biases, et cetera, I could’ve gone on for another three hours with him I barely got to scratch the surface of all my questions.

With no further ado, my conversation with Tom Gilovich.

I have an extra special guest this week and his name is Professor Thomas Gilovich, he is a professor of psychology at Cornell University, he has published numerous peer-reviewed works on cognition and heuristics and is among the most cited academics in the field working on behavioral psychology and economics.

His work has debunked to the idea of the hot hand in basketball, the spotlight affect, the bias blind site, clustering allusion, and numerous other cognitive issues are in his purview, he is the author of numerous books including a textbook on heuristics and biases. He is the co-author of “Why Smart People Make Big Money Mistakes And What You Can Do To Correct Them.”

I became familiar with his work “How We Know What Isn’t So: The Fallibility of Human Reason in Everyday Life.” It was one of the first popular books on behavioral economics and one that I found incredibly influential. Thomas Gilovich, welcome to Bloomberg.


RITHOLTZ: Let’s start out with how we know what isn’t so, you begin the book with a quote from Artemis Ward, “It ain’t the things we don’t know that get us into trouble, it’s the things we know that just ain’t so.” Tell us about that.

GILOVICH: Well if we are convinced of things that aren’t true, we’re going to go down certain paths that aren’t going to be productive and that quote captures that idea very well. And what I like about that quote is that most people attribute it to Mark Twain or Will Rogers, the source I thought at the time was Artemis Ward and maybe two years after the publication of a book, a reader wrote to me and said well it’s really interesting, you are sort of telling everyone they got it wrong with Will Rogers and Mark Twain, but in fact it goes back even earlier than Artemis Ward to someone named Josh Billings that I hadn’t heard of.

So it’s kind of ironic starting off a book on allusion and error, the very first sentence of the book contains a citation error at least.

RITHOLTZ: But that citation error did not lead you down a path filled with errors, let’s discuss a little bit about the things that you discovered and published in the book and we’ll begin with a very simple question, what are heuristics and biases?

GILOVICH: Well the bias part is quite easy it’s a term that people are familiar with when there’s a systematic departure between a belief that you have in reality or a tendency to choose to veer in one direction when you should be bearing in another direction, so it’s a systematic departure from reality or the best assessment of reality.

Heuristics is a little more complicated for most folks, it’s generally defined in the behavioral economics world as a rough approximation seat-of-the-pants —

RITHOLTZ: Rule of thumb.

GILOVICH: Rule of thumb is another way of describing it, yes.

RITHOLTZ: And why do these heuristics lead people down the wrong path?

GILOVICH: Because they generally work pretty well in one of the earliest examples used and applied to psychology was Daniel Kahnemann and Amos Tversky’s examples of we use the clarity of an image as a cue for how far away it is. The farther things are, the harder it is to see them clearly, so they will see more indistinct and that generally works pretty well. We’re able to see whether something very far away is relatively close up.

But on a hazy day, that’s going to make things seem farther away than they really are and conversely on a spectacularly clear day, you often have the reaction of whoa those mountains are — I didn’t realize they were that close.

RITHOLTZ: Let’s use another example of some biases, you’re in line at the supermarket and your line doesn’t seem to be moving, the line next to you really looks like it’s flying, are you waiting for a tollbooth? I know parts of the country still have tollbooths and you switch lines and suddenly the line you’re in comes to a dead halt and the line you just left seems to be moving.

What is it about our life experience that causes that illusion or is it an illusion?

GILOVICH: That one as far as the grocery lines, I don’t know if anyone has formally studied it, but you have to ask what principle of the universe would there be that would systematically distort things such that whenever you moved to a line, it would slow down and the line that you are in suddenly sped up.

But it’s easy to explain why people would believe that, even if it’s not true. That is to say those times when you stay in your overly busy line, you’re tempted to move to another one and it turns out that that you can see that that would’ve been a better thing, your line stays slow, you can see people speeding through the other line, that bothers you, but you get over it.

If on the other hand you make the opposite mistake — you switch to the seemingly speedy line and it slows down — the line you are in suddenly speeds up, you are going to kick yourself and say why did I do that? I was in the right line, I brought this on myself and it’s more annoying and because it’s more annoying, it’s more memorable and therefore you’re going to have a distorted sense in your head of how common it is.

Very much like there’s a belief in the sports world, the baseball world that if your team, your pitcher has a no-hitter in progress, don’t comment on it and that’s partly fed by the idea that if your pitcher does have a no-hitter and you say, oh we got a no-hitter going this is great and then they lose it, you draw an association between those two and those are going to stand out and you are going to think that it’s what you’ve done has played some determined role which of course hasn’t.

RITHOLTZ: So let’s talk a little about mean reversion. Very often, when we see things that are outliers to the upset or the downside, we’re sort of surprised when the next item in that series is not as extreme be it how fast the line is moving or how easily a no-hitter is lost and goes back to normal — issues why do people have such a hard time with mean reversion?

GILOVICH: That’s a great question and there are a number of things that contribute to this belief, the failure to recognize the fact that regression to the mean is happening and one of them is that it is the same story that I’ve described before which is when it reverts and particularly when it reverts after you’ve done something that that stands out in your memory more and distorts your — the intuitive database that you have in your head.

And so there a lot of superstitions that are essentially a failure to recognize the operational progression, the Sports Illustrated Jinx being one of them that it’s believed that if you get your picture on the cover of Sports Illustrated, but that’s bad luck and doesn’t take that much insight to recognize that really is a mean aversion account that is you only get your picture on the cover of Sports Illustrated if you had a run of success and extraordinary success at time one is going to be followed not by abject failure but by somewhat less extreme run of success afterwards.

So you’re right there on the peak on average people to do less well the next time that gives rise to this belief that it’s bad luck to be pictured on the cover of Sports Illustrated magazine. And people — athletes truly believe it, some of them have turned down the opportunity to be on the cover of Sports Illustrated simply because they thought it was bad luck.

RITHOLTZ: Let’s talk a little about the hot hands in basketball. You first wrote about this affect with the Amos Tversky, is about 30 years ago, is that right?

GILOVICH: Yes, the paper came out in 1985.


10 Tuesday AM Reads

My two for Tuesday morning train pool reads:

• One of 2016’s Worst-Performing Assets: Frontier Markets (Wall Street Journal)
• A Portrait of the Investing Columnist as a (Very) Young Man (Jason Zweigsee also Ten Things I’ve Learned From Blogging (Fortune Financial)
• Five doable strategies for financial success in 2017 (USA Today)
• Rationally Irrational (Irrelevant Investor) see also The Hierarchy of Investment Difficulty (A Wealth of Common Sense)
• Some States Create Lots of Jobs But Lose People (BloombergView)
• Soaring insulin prices are a case in point: A ‘free market’ in healthcare is doomed (LA Times) see also Carl Icahn Is a Good Investor. But He Has No Business Being Donald Trump’s Regulatory Scourge. (Slate)
• The US Is Vulnerable to Drone Attacks. Here’s How to Stop Them (Wired)
• Will somebody please give Norm Macdonald another TV show? (Washington Post) see also Norm Macdonald Tweeted About The SNL Anniversary & You’re Gonna Want To Read Every Word (Gothamist)
• Google’s top results for “Did the Holocaust happen” now expunged of denial sites (Search Engine Land)
• Rogue One: an ‘Engineering Ethics’ Story (SciFi Policysee also One of the best performances in ‘Rogue One’ is by an actor who died in 1994 (SF Gate)

Be sure to check out our Masters in Business interview this weekend with Bruce Tuchman, who brought numerous US cable channels — Nickelodeon, AMC Global, MGM, Sundance, etc to a global audience.



Bite back at your taxes: hold on to more of your earnings

Source: Quartz

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Reflections on Macroeconomics Then and Now

Reflections on Macroeconomics Then and Now
Vice Chairman Stanley Fischer
At the “Policy Challenges in an Interconnected World”
32nd Annual National Association for Business Economics Economic Policy Conference
Washington, D.C. March 7, 2016




I am grateful to the National Association for Business Economics (NABE) for conferring the fourth annual NABE Paul A. Volcker Lifetime Achievement Award for Economic Policy on me, thereby allowing me the honor of following in the footsteps of Paul Volcker, Jean-Claude Trichet, and Alice Rivlin.1 The honor of receiving the award is enhanced by its bearing the name of Paul Volcker, a model citizen and public servant, and a giant in every sense among central bankers.

One thinks of many things on an occasion such as this one. My mind goes back first to growing up in a very small town in Zambia, then Northern Rhodesia, and to the surprise and delight my parents would have felt at seeing me standing where I am now. They would have been even more delighted that my girlfriend, Rhoda, whom I met when my parents moved to a bigger town in Zimbabwe, and I have been happily married for 50 years. But that is not the story I will tell today. Rather, I want to talk about our field, macroeconomics, and some of the lessons we have learned in the course of the last 55 years–and I say 55 years, because in 1961, at the end of my school years, on the advice of a friend, I read Keynes’s General Theory for the first time.

Did I understand it? Certainly not. Was I captivated by it? Certainly, though “captured” is a more appropriate word than “captivated.” Does it remain relevant? Certainly. Just a week ago I took it off the bookshelf to read parts of chapter 23, “Notes on Mercantilism, the Usury Laws, Stamped Money and Theories of Under-Consumption.” Today that chapter would be headed “Protectionism, the Zero Lower Bound, and Secular Stagnation,” with the importance of usury laws having diminished since 1936.

There is an old joke about our field–not the one about the one-handed economist, nor the one about “assume you have a can opener,” nor the one that ends, “If I were you, I wouldn’t start from here.” Rather it’s the one about the Ph.D. economist who returns to his university for his class’s 50th reunion. He asks if he can see the most recent Ph.D. generals exam. After a while it is brought to him. He reads it carefully, looking perplexed, and then says, “But this is exactly the same as the exam I wrote over 50 years ago.” “Ah yes,” says the professor. “It is the same, but all the answers are different.”

Is that really the case? Not really, though it is true to some extent in the realm of policy. To discuss the question of whether the answers to the questions of how to deal with macroeconomic policy problems have changed markedly over the past half-century or so, I will start by briefly sketching the structure of a basic macro model. The building blocks of this model are similar to those used in many macro models, including FRB/US, the Fed staff’s large-scale model, and a variety of DSGE (dynamic stochastic general equilibrium) models used at the Fed and other central banks and by academic researchers.

The structure of the model starts with the standard textbook equation for aggregate demand for domestically produced goods, namely:2

  1. AD=C+I+G+NX;
  2. Next is the wage-price block, which is based on a wage or price Phillips curve. Okun’s law is included to make the transition between output and employment;
  3. Monetary policy is described by a money supply or interest rate rule;
  4. The credit markets and financial intermediation are built off links between the policy interest rate and the rates of return on, and/or demand and supply functions for, other assets;
  5. The balance of payments and the exchange rate enter through the balance of payments identity, namely that the current account surplus must be equal to the capital account deficit, corrected for official intervention;
  6. Dynamics of stocks: There are dynamic equations for the capital stock, the stock of government debt, and the external debt.

When I was an undergraduate at the London School of Economics (LSE) between 1962 and 1965, we learned the IS-LM model, which combined the aggregate demand equation (1) with the money market equilibrium condition set out in (3). That was the basic understanding of the Keynesian model as crystallized by John Hicks, Franco Modigliani, and others, in which it was easy to add detail to the demand functions for private-sector consumption, C; for investment, I; for government spending, G; and for net exports. The Keynesian emphasis on aggregate demand and its determinants is one of the basic innovations of the Keynesian revolution, and one that makes it far easier to understand and explain what factors are determining output and employment.

Continuing down the list, on price and wage dynamics, the Phillips curve has flattened somewhat since the 1950s and 1960s.3 Further, the role of expectations of inflation in the Phillips curve has been developed far beyond what was understood when A.W. Phillips–who was a New Zealander, an LSE faculty member, and a statistician and former engineer–discovered what later became the Phillips curve. The difference between the short- and long-run Phillips curves, which is now a staple of textbooks, was developed by Milton Friedman and Edmund Phelps, and the effect of making expectations rational or model consistent was emphasized by Robert Lucas, whose islands model provided an imperfect information reason for a nonvertical short-run Phillips curve. In Okun’s law, the Okun coefficient–the coefficient specifying how much a change in the unemployment rate affects output–appears to have declined over time. So has the trend rate of productivity growth, which is a critical determinant of future levels of per capita income.

In (3), the monetary equilibrium condition, the monetary policy decision was typically represented by the money stock at the LSE and perhaps also at the Massachusetts Institute of Technology (MIT) after the Keynesian revolution (after all, “L” represents the liquidity preference function and “M” the supply of money); now the money supply rule is replaced by an interest-rate setting rule, for instance a reaction function of some form, or by a calculated “optimal” policy based on a loss function.

The development of the flexible inflation-targeting approach to monetary policy is one of the major achievements of modern macroeconomics. Flexible inflation targeting allows for flexibility in the speed with which the monetary authority plans on returning to the target inflation rate, and is thereby close to the dual mandate that the law assigns to the Fed.

A great deal of progress has been made in developing the credit and financial intermediation block. As early as the 1960s, each of James Tobin, Milton Friedman, and Karl Brunner and Alan Meltzer wrote out models with more fully explicated financial sectors, based on demand functions for assets other than money. Later the demand functions were often replaced by pricing equations derived from the capital asset pricing model. Researchers at the Fed have been bold enough to add estimated term and risk premiums to the determination of the returns on some assets.4 They have concluded, inter alia, that the arguments we used to make about how easy it would be to measure expected inflation if the government would introduce inflation-indexed bonds failed to take into account that returns on bonds are affected by liquidity and risk premiums. This means that one of the major benefits that were expected from the introduction of inflation-indexed bonds (Treasury Inflation-Protected Securities, generally called TIPS), namely that they would provide a quick and reliable measure of inflation expectations, has not been borne out, and that we still have to struggle to get reasonable estimates of expected inflation.

As students, we included NX, net exports, in the aggregate demand equation, but we did not generally solve for the exchange rate, possibly because the exchange rate was typically fixed. Later, in 1976, Rudi Dornbusch inaugurated modern international macroeconomics–and here I’m quoting from a speech by Ken Rogoff–in his famous overshooting model.5 As globalization of both goods and asset markets intensified over the next 40 years, the international aspects of trade in goods and assets occupied an increasingly important role in the economies of virtually all countries, not least the United States, and in macroeconomics.

At the LSE, we took a course on the British economy from Frank Paish, whose lectures consisted of a series of charts, accompanied by narrative from the professor. He made a strong impression on me in a lecture in 1963, in which he said, “You see, it (the balance of payments deficit) goes up and it goes down, and it is clear that we are moving toward a balance of payments crisis in 1964.” I waited and I watched, and the crisis appeared on schedule, as predicted. But Paish also warned us that forecasting was difficult, and gave us the advice “Never look back at your forecasts–you may lose your nerve.” I pass that wisdom on to those of you who need it.

I remember also my excitement at being told by a friend in a more senior class about the existence of econometric models of the entire economy. It was a wonderful moment. I understood that economic policy would from then on be easy: All that was necessary was to feed the data into the model and work out at what level to set the policy parameters. Unfortunately, it hasn’t worked out that way. On the use of econometric models, I think often of something Paul Samuelson once said: “I’d rather have Bob Solow’s views than the predictions of a model. But I’d rather have Solow with a model than without one.”

We learned a lot at the LSE. But wonderful as it was to be in London, and to meet people from all over the world for the first time, and to be able to travel to Europe and even to the Soviet Union with a student group, and to ski for the first time in my life in Austria, it gradually became clear to me that the center of the academic economics profession was not in London or Oxford or Cambridge, but in the United States.

There was then the delicate business of applying to graduate school. There was a strong Chicago tendency among many of the lecturers at the LSE, but I wanted to go to MIT. When asked why, I gave a simple answer: “Samuelson and Solow.” Fortunately, I got into MIT and had the opportunity of getting to know Samuelson and Solow and other great professors. And I also met the many outstanding students who were there at the time, among them Robert Merton. I took courses from Samuelson and Solow and other MIT stars, and I wrote my thesis under the guidance of Paul Samuelson and Frank Fisher. From there, my first job was at the University of Chicago–and I understood that I was very lucky to have been able to learn from the great economists at both MIT and Chicago. Among the many things I learned at Chicago was a Milton Friedman saying: “Man may not be rational, but he’s a great rationalizer,” which is a quote that often comes to mind when listening to stock market analysts.

After four years at Chicago, I returned to the MIT Department of Economics, and thought that I would never leave–even more so when MIT succeeded in persuading Rudi Dornbusch, whom I had met when he was a student at Chicago, to move to MIT–thus giving him too the benefit of having learned his economics at both Chicago and MIT, and giving MIT the pleasure and benefit of having added a superb economist and human being to the collection of such people already present.

MIT was still heavily involved in developing growth theory at the time I was a Ph.D. student there, from 1966 to 1969. We students were made aware of Kaldor’s stylized factsabout the process of growth, presented in his 1957 article “A Model of Economic Growth.” They were:

  1. The shares of national income received by labor and capital are roughly constant over long periods of time.
  2. The rate of growth of the capital stock per worker is roughly constant over long periods of time.
  3. The rate of growth of output per worker is roughly constant over long periods of time.
  4. The capital/output ratio is roughly constant over long periods of time.
  5. The rate of return on investment is roughly constant over long periods of time.
  6. The real wage grows over time.

Well, that was then, and many of the problems we face in our economy now relate to the changes in the stylized facts about the behavior of the economy: Every one of Kaldor’s stylized facts is no longer true, and unfortunately the changes are mostly in a direction that complicates the formulation of economic policy.6

While the basic approach outlined so far remains valid, and can be used to address many macroeconomic policy issues, I would like briefly to take up several topics in more detail. Some of them are issues that have remained central to the macroeconomic agenda over the past 50 years, some have to my regret fallen off the agenda, and others are new to the agenda.

  1. Inflation and unemployment: Estimated Phillips curves appear to be flatter than they were estimated to be many years ago–in terms of the textbooks, Phillips curves appear to be closer to what used to be called the Keynesian case (flat Phillips curve) than to the classical case (vertical Phillips curve). Since the U.S. economy is now below our 2 percent inflation target, and since unemployment is in the vicinity of full employment, it is sometimes argued that the link between unemployment and inflation must have been broken. I don’t believe that. Rather the link has never been very strong, but it exists, and we may well at present be seeing the first stirrings of an increase in the inflation rate–something that we would like to happen.
  2. Productivity and growth: The rate of productivity growth in the United States and in much of the world has fallen dramatically in the past 20 years. The table shows calculated rates of annual productivity growth for the United States over three periods: 1952 to 1973; 1974 to 2007; and the most recent period, 2008 to 2015. After having been 3 percent and 2.1 percent in the first two periods, the annual rate of productivity growth has fallen to 1.2 percent in the period since the start of the global financial crisis.The right guide to thinking in this case is given by a famous Herbert Stein line: “The difference between a growth rate of 1 percent and 2 percent is 100 percent.” Why? Productivity growth is a major determinant of long-term growth. At a 1 percent growth rate, it takes income 70 years to double. At a 2 percent growth rate, it takes 35 years to double. That is to say, that with a growth rate of 1 percent per capita, it takes two generations for per capita income to double; at a 2 percent per capita growth rate, it takes one generation for per capita income to double. That is a massive difference, one that would very likely have severe consequences for the national mood, and possibly for economic policy. That is to say, there are few issues more important for the future of our economy, and those of every other country, than the rate of productivity growth.At this stage, we simply do not know what will happen to productivity growth.Robert Gordon of Northwestern University has just published an extremely interesting and pessimistic book that argues we will have to accept the fact that productivity will not grow in future at anything like the rates of the period before 1973. Others look around and see impressive changes in technology and cannot believe that productivity growth will not move back closer to the higher levels of yesteryear.7 A great deal of work is taking place to evaluate the data, but so far there is little evidence that data difficulties account for a significant part of the decline in productivity growth as calculated by the Bureau of Labor Statistics.8
  3. The ZLB and the effectiveness of monetary policy: From December 2008 to December 2015, the federal funds rate target set by the Fed was a range of 0 to 1/4 percent, a range of rates that was described as the ZLB (zero lower bound).9Between December 2008 and December 2014, the Fed engaged in QE–quantitative easing–through a variety of programs. Empirical work done at the Fed and elsewhere suggests that QE worked in the sense that it reduced interest rates other than the federal funds rate, and particularly seems to have succeeded in driving down longer-term rates, which are the rates most relevant to spending decisions.Critics have argued that QE has gradually become less effective over the years, and should no longer be used.It is extremely difficult to appraise the effectiveness of a program all of whose parameters have been announced at the beginning of the program. But I regard it as significant with respect to the effectiveness of QE that the taper tantrum in 2013, apparently caused by a belief that the Fed was going to wind down its purchases sooner than expected, had a major effect on interest rates.More recently, critics have argued that QE, together with negative interest rates, is no longer effective in either Japan or in the euro zone.That case has not yet been empirically established, and I believe that central banks still have the capacity through QE and other measures to run expansionary monetary policies, even at the zero lower bound.
  4. The monetary-fiscal policy mix: There was once a great deal of work on the optimal monetary-fiscal policy mix. The topic was interesting and the analysis persuasive. Nonetheless the subject seems to be disappearing from the public dialogue; perhaps in ascendance is the notion that–except in extremis, as in 2009–activist fiscal policy should not be used at all. Certainly, it is easier for a central bank to change its policies than for a Treasury or Finance Ministry to do so, but it remains a pity that the fiscal lever seems to have been disabled.
  5. The financial sector: Carmen Reinhart and Ken Rogoff’s book, This Time Is Different, must have been written largely before the start of the great financial crisis. I find their evidence that a recession accompanied by a financial crisis is likely to be much more serious than an ordinary recession persuasive, but the point remains contentious. Even in the case of the Great Recession, it is possible that the U.S. recession got a second wind when the euro-zone crisis worsened in 2011. But no one should forget the immensity of the financial crisis that the U.S. economy and the world went through following the bankruptcy of Lehman Brothers–and no one should forget that such things could happen again.The subsequent tightening of the financial regulatory system under the Dodd-Frank Act was essential, and the complaints about excessive regulation and excessive demands for banks to hold capital betray at best a very short memory.We, the official sector and particularly the regulatory authorities, do have an obligation to try to minimize the regulatory and other burdens placed on the private sector by the official sector–but we have a no less important obligation to try to prevent another financial crisis. And we should also remember that the shadow banking system played an important role in the propagation of the financial crisis, and endeavor to reduce the riskiness of that system.
  6. The economy and the price of oil: For some time, at least since the United States became an oil importer, it has been believed that a low price of oil is good for the economy. So when the price of oil began its descent below $100 a barrel, we kept looking for an oil-price-cut dividend. But that dividend has been hard to discern in the macroeconomic data. Part of the reason is that as a result of the rapid expansion of the production of oil from shale, total U.S. oil production had risen rapidly, and so a larger part of the economy was adversely affected by the decline in the price of oil. Another part is that investment in the equipment and structures needed for shale oil production had become an important component of aggregate U.S. investment, and that component began a rapid decline. For these reasons, although the United States has remained an oil importer, the decrease in the world price of oil had a mixed effect on U.S. gross domestic product. There is reason to believe that when the price of oil stabilizes, and U.S. shale oil production reaches its new equilibrium, the overall effect of the decline in the price of oil will be seen to have had a positive effect on aggregate demand in the United States, since lower energy prices are providing a noticeable boost to the real incomes of households.
  7. Secular stagnation: During World War II in the United States, many economists feared that at the end of the war, the economy would return to high pre-war levels of unemployment–because with the end of the war, demobilization, and the massive reduction that would take place in the defense budget, there would not be enough demand to maintain full employment.Thus was born or renewed the concept of secular stagnation–the view that the economy could find itself permanently in a situation of low demand, less than full employment, and low growth.10 That is not what happened after World War II, and the thought of secular stagnation was correspondingly laid aside, in part because of the growing confidence that intelligent economic policies–fiscal and monetary–could be relied on to help keep the economy at full employment with a reasonable growth rate.Recently, Larry Summers has forcefully restated the secular stagnation hypothesis, and argued that it accounts for the current slowness of economic growth in the United States and the rest of the industrialized world. The theoretical case for secular stagnation in the sense of a shortage of demand is tied to the question of the level of the interest rate that would be needed to generate a situation of full employment. If the equilibrium interest rate is negative, or very small, the economy is likely to find itself growing slowly, and frequently encountering the zero lower bound on the interest rate.Research has shown a declining trend in estimates of the equilibrium interest rate. That finding has become more firmly established since the start of the Great Recession and the global financial crisis.11 Moreover, the level of the equilibrium interest rate seems likely to rise only gradually to a longer-run level that would still be quite low by historical standards.

    What factors determine the equilibrium interest rate? Fundamentally, the balance of saving and investment demands. Several trends have been cited as possible factors contributing to a decline in the long-run equilibrium real rate. One likely factor is persistent weakness in aggregate demand. Among the many reasons for that, as Larry Summers has noted, is that the amount of physical capital that the revolutionary information technology firms with high stock market valuations have needed is remarkably small. The slowdown of productivity growth, which as already mentioned has been a prominent and deeply concerning feature of the past six years, is another important factor.12 Others have pointed to demographic trends resulting in there being a larger share of the population in age cohorts with high saving rates.13 Some have also pointed to high saving rates in many emerging market countries, coupled with a lack of suitable domestic investment opportunities in those countries, as putting downward pressure on rates in advanced economies–the global savings glut hypothesis advanced by Ben Bernanke and others at the Fed about a decade ago.14

    Whatever the cause, other things being equal, a lower level of the long-run equilibrium real rate suggests that the frequency and duration of future episodes in which monetary policy is constrained by the ZLB will be higher than in the past. Prior to the crisis, some research suggested that such episodes were likely to be relatively infrequent and generally short lived.15 The past several years certainly require us to reconsider that basic assumption. Moreover, recent experience in the United States and other countries has taught us that conducting monetary policy at the effective lower bound is challenging.16 And while unconventional policy tools such as forward guidance and asset purchases have been extremely helpful and effective, all central banks would prefer a situation with positive interest rates, restoring their ability to use the more traditional interest rate tool of monetary policy.17

    The answer to the question “Will the equilibrium interest rate remain at today’s low levels permanently?” is also that we do not know. Many of the factors that determine the equilibrium interest rate, particularly productivity growth, are extremely difficult to forecast. At present, it looks likely that the equilibrium interest rate will remain low for the policy-relevant future, but there have in the past been both long swings and short-term changes in what can be thought of as equilibrium real rates.

    Eventually, history will give us the answer. But it is critical to emphasize that history’s answer will depend also on future policies, monetary and other, notably including fiscal policy.

Concluding Remarks
Well, are the answers all different than they were 50 years ago? No. The basic framework we learned a half-century ago remains extremely useful. But also yes: Some of the answers are different because they were not on previous exams because the problems they deal with were not evident fifty years ago. So the advice to potential policymakers is simple: Learn as much as you can, for most of it will come in useful at some stage of your career; but never forget that identifying what is happening in the economy is essential to your ability to do your job, and for that you need to keep your eyes, your ears, and your mind open, and with regard to your mouth–to use it with caution.

Many thanks again for this award and this opportunity to speak with you.

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——– (2014). “Interpreting Deviations from Okun’s Law,” Leaving the Board FRBSF Economic Letter 2014-12. San Francisco: Federal Reserve Bank of San Francisco.

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Mokyr, Joel, Chris Vickers, and Nicolas L. Ziebarth (2015). “The History of Techonological Anxiety and the Future of Economic Growth: Is This Time Different?” Journal of Economic Perspectives, vol. 29 (Summer), pp. 31-50.

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1. I am grateful to David Lopez-Salido, Andrea Ajello, Elmar Mertens, Stacey Tevlin, and Bill English of the Federal Reserve Board for their assistance. Views expressed are mine, and are not necessarily those of the Federal Reserve Board or the Federal Open Market Committee. Return to text

2. A fuller description of the equations is contained in the appendix. Return to text

3. See Blanchard (2016). Return to text

4. See D’Amico, Kim, and Wei (2014). Return to text

5. See Dornbusch (1976) and Rogoff (2001). Return to text

6. See Jones and Romer (2010). Return to text

7. See, for instance, Mokyr, Vickers, and Ziebarth (2015). Return to text

8. See Byrne, Fernald, and Reinsdorf (forthcoming). Return to text

9. Inside the Fed, the range of 0 to 1/4 percent is generally called the ELB, the effective lower bound. Return to text

10. I am distinguishing in this section between secular stagnation as being caused by a deficiency of aggregate demand and another view, that output growth will be very slow in future because productivity growth will be very low. The view that future productivity growth will be very low has already been discussed, with the conclusion that we do not have a good basis for predictions of its future level, and that we simply do not know whether future productivity growth will be extremely low or higher than it has been recently. There is no shortage of views on this issue among economists, but the views to some extent appear to depend on whether the economist making the prediction is an optimist or a pessimist. Return to text

11. This research includes recent work by Johannsen and Mertens (2015) and Kiley (2015) that uses extensions of the original Laubach and Williams (2003) framework. An international perspective on medium-to-long-run real interest rates is provided by U.S. Executive Office of the President (2015). Reinhart and Rogoff (2009) and Hall (2014) discuss the long-lived effects of financial crises on economic performance. See also Hamilton and others (2015). I have, in addition, drawn on Fischer (forthcoming). Return to text

12. It is also a major factor explaining the phenomenon of the economy’s impressive performance on the jobs front during a period of historically slow growth. Return to text

13. See, for instance, Gordon (2014, 2016). Return to text

14. See Bernanke (2005). See also the recent work by Caballero, Farhi, and Gourinchas (2008); and Mendoza, Quadrini, and Rios-Rull (2009). Return to text

15. See, for instance, Reifschneider and Williams (2000), Blanchard and Simon (2001), and Stock and Watson (2003). Return to text

16. For a discussion of various issues reviewed by the Federal Open Market Committee in late 2008 and 2009 regarding the complications of unconventional monetary policy at the ZLB, see the set of staff memos on the Board’s website. Return to text

17. See Williams (2013). Return to text

Accessible Version


The following model includes a number of elements that play a central role in the analysis of economic fluctuations and in larger policy models as I have encountered them at the Federal Reserve and other institutions.

An aggregate demand relationship, in the form of the investment-saving (or IS) curve, characterizes the negative dependence of economic activity on the real borrowing rate, iBπe, and the positive dependence on expected output, ye; government spending, G; and net exports, NX, as a function of the exchange rate, e, and foreign output, yf:

y=A(iBπe,ye,G,NX), with NX=f(ePfP,yf). (1)

The Phillips curve describes a relationship between inflation and labor market slack. Inflation responds negatively to the level of the unemployment gap, u^, and to changes in aggregate productivity, z (including shocks to commodity prices). Current inflation also responds positively to expected future inflation, πe, and to the level of the borrowing rate and of the exchange rate, ϕ(iB,e) (cost – push shocks):

π=f(u^)z+πe+ϕ(iB,e). (Phillips curve) (2)

Current issues regarding the role of expectations, the size of the slope, and the pass-through from exchange rate movements to domestic prices and wages can be addressed in this context. Recent discussions can be found in Blanchard’s (2016) reference to the back-to-the-1960s thinking about the Phillips curve and in Blanchard, Cerutti, and Summers’s (2015) thoughts on hysteresis mechanisms underlying the inflation and unemployment dynamic.

To connect cyclical fluctuations in the level of aggregate activity with changes in employment, Okun’s law has been proved useful as an empirical description of the relationship between the output gap and the unemployment gap, u^=uuu (see Knotek (2007); and Daly, Fernald, Jordà, and Nechio (2013, 2014)):

y^=γu^. (Okun’s law) (3)

To characterize monetary policy, it is nowadays useful to consider how the central bank affects the level of interest rates by setting the federal funds rate, i, in response to deviations of expected inflation from its target, π^=πeπ (inflation gap), and percent deviations of output from its potential level, y^=yyy (output gap):

i=f(πeπ,y^). (4)

To capture the role of credit and financial intermediation in the economy, consider a loan market equation, where the demand on the left-hand side depends negatively on the borrowing rate, iB, and the level of economic activity (higher income implies lower financing needs). The supply of loans depends negatively on the level of interest rates, i, and positively on the level of intermediation spreads, ω, and income, y (to the extent that higher aggregate income increases deposits and banks’ capitalization and hence the supplies of intermediated funds). This equation pins down the equilibrium level of the intermediation spread, ω:

Ld(iB,y)=Ls(i,ω,y). (5)

The borrowing rate, iB, is then equal to the sum of the risk-free rate set by the central bank, i, and the spread, ω:

iB=i+ω. (6)

This analysis is in the spirit of James Tobin’s approach to monetary economics as was recently described by Solow (2004) and extended by Woodford (2010) to describe the role for financial intermediation shocks. It also captures the work by B. Friedman, B. Bernanke, and A. Blinder in thinking about the role of credit and credit spreads in the transmission of monetary policy impulses.

Open economy aspects are captured by the following equations: The balance of paymentis in equilibrium when net exports are compensated by capital flows of opposite sign. Capital flows depend on the difference between the domestic interest rate and the foreign rate adjusted for depreciation:

NX(ePfP,yf)+CF(iifdee)=0. (7)

Furthermore, the uncovered interest rate parity equates the rate of return on domestic assets, i, to the rate of return on foreign assets, if, plus future expected changes in the exchange rate, dee, and a residual risk premium component, RP:

i=if+dee+RP. (8)

For a detailed description of these relationships, see Dornbusch, Fischer, and Startz (2014); and Obstfeld and Rogoff (1996).



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