As you all may know, Norwegians have mixed feelings about Swedes.
But one of the good things about the Swedes: They know how to pronounce my last name correctly.
So, I was here two years ago and I enjoyed very much speaking to the students then as well.
Two years ago I simply gave my most recent paper in forty minutes and that's pretty fast for a paper.
Generally, when you give a seminar you have an hour and a half.
And so, at the NSF luncheon one of the students was nice enough to give me some feedback.
He said it was interesting to hear the paper, but it's tough to get much out of it in such a short period of time.
And so he said, to him it would have been more interesting
if I would have talked about how the two papers came about that the Nobel Committee decided.
And I thought about that.
And so I've decided that's what I will talk about for the first roughly third of the talk.
Then, in the second third, I'll talk about what I regard as the long run implications
of what we can learn from especially the second paper today.
And finally, I can't resist to still tell you something about my most recent paper.
And of course that's a different paper than the one I talked about two years ago.
Obviously I won't be able to tell you that much.
But I'll tell you about the motivation for the paper, I'll show you some pictures and some graphs,
be clear on the question we address.
And give you the main gist of the findings.
So, how did these two papers come about?
Personally I regard the second, the Econometric 1982 as the one that had the most long lasting implications.
The first time inconsistency obviously gets referred to sometimes as a potential source
for argument for independent central banks for example.
That's important, a practical implication.
So, an important feature of that paper is the fact that economic policy, optimal policy is time inconsistency,
there's that dynamic inconsistency problem.
And so the first question is: When did I first run across dynamic inconsistency? Well, as a PhD student.
By the way, the message of this first part of the talk is that these results didn't come about as kind of a flash, just like that.
Lots of grungy work was needed before really the message became clear.
Looking around for a thesis topic I had read some papers in a literature called "the assignment problem".
Kind of a popular literature at the time.
And the idea was that the government has certain instruments and the government may have certain goals.
And the idea was that some instruments were more appropriate for some goals than others.
How to assign instruments to goals?
And sometimes there was a division between monetary policy instruments and fiscal policy instruments.
I actually thought that literature was kind of silly.
So I was thinking about what would make more sense.
And I came up with the idea of formulating that problem as a dynamic game.
And the idea was monetary and fiscal policy makers they all had their objectives
and they would have different weights on the arguments of their objectives.
Quite a reasonable thing to do.
And furthermore thinking about the way policy is done, at least in the United States.
Fiscal policy going through congress and so on and monetary policy
is simply being done day by day almost or at least month by month by the Federal Reserve.
It made sense to me to make this game what I called a dominant player game.
Some people call it the Stackelberg game.
And the basic features that the dominant player moves first and the other players are followers.
Well, this was a dynamic game.
And right there I saw the time inconsistency, the policy of the dominant player,
the fiscal policy maker in my model was clearly, the optimal policy was time inconsistent.
Well, I didn't make much of it,
I simply argued that the recursive time consistent policy there was a natural candidate for an equilibrium
and that's what I focused on.
I also had an example in my thesis of a dominant firm example, a dominant firm oligopoly.
So, the key was to-at the stage when I was done I had been working with Prescott a lot.
I was fortunate enough that he showed up as a faculty member two years before I was done.
I ran into him in the hallway.
He asked me what I was working on and I told him and he expressed a great deal of interest.
It turned out he had done some game theory himself.
And so at the end of my studies, in fact even before the thesis defense
we came across an important paper by Robert Lucas called "Econometric Policy Evaluation: A Critique".
That was an astounding paper to us.
It seemed very important.
He had three quite graphic examples of his message, one being an investment tax credit example.
And we decided to pick up on-take Lucas a step further and think of the policy making,
policy being chosen optimally in a framework, more or less like Robert Lucas's investment tax credit example.
And so the key was to realize that in that sort of set up clearly the policy maker, the government was a dominant player.
The rest of the economy being atomistic of course, there was no reverse,
strategic power from people to the government except in the aggregate maybe.
No individual power.
And even then, in the beginning, we didn't make that much out of the time inconsistency.
Because of the focus on the recursive equilibrium I had not even ever calculated an optimal policy.
That was a much tougher thing to do.
Not easy to do using dynamic programming for example.
But instead we focused more on the fact that at the time there was this belief
that optimal control theory had solved all of economists problems.
I remember there was a two page article in Business Week at the time, talking about:
Now economists could write down models of the economy.
They could set up an objective, calculate the best policy and then: voila.
So, at some point I decided to have a research assistant calculate the optimal policy,
in a simple example actually, a dominant firm example.
A very good research assistant by the name of Nina Bjerkedal who is now at the Finance Department in Norway,
almost at the top of their hierarchy.
I was very disappointed she didn't go on for a PhD.
But I had her calculate the optimal policy using brute force and compared with the time consistent.
And the difference was amazing.
And that's really when it struck us that that's a big issue.
And then from there on we set up a model with considerable dynamics in it.
And we found to our amazement that the time consistent policy could be very bad.
To us that was a very important finding.
Now, back to the optimal control thing.
So, the first time I gave the paper at the conference in Cambridge, Massachusetts.
This was a conference where half the people were control theorists and half the people were economists and so on.
We had a more provocative title than it finally ended up.
The first version of the paper was called "On the Inapplicability of Optimal Control to Policy Making".
Well, I got up to talk about this paper and all hell broke loose.
The rest of the session pretty much consisted in everyone trying to figure out
where is the mistake-there's got to be a mistake here somewhere.
Of course I was pretty confident from all my other work with dominant players that there's no mistake here.
And so, I suppose one message to students is: If people think you came up with something that's wrong,
well, you'd better check that it is correct.
But if it is correct, that could be a sign that you are onto something quite important.
Going from that paper to the "Time-to-Build-paper"-in some sense mechanically it was an easy step.
But the main thinking was how to approach the problem,
or how to approach it in such a way that we could come up with good quantitative answers.
We knew we had to calculate competitive equilibriums, dynamic competitive equilibriums.
And in fact we were able to use some of the same programs that we had used for the "Time-Inconsistency-paper".
There as well we calculated dynamic equilibriums.
So that wasn't the biggest problem.
The question we wanted to address,
something everyone around that time thought that the main driving forces for the business cycle had to be something-
either monetary shocks or fiscal shocks.
So we decided to check out the following question:
Suppose technological shocks were the only source of fluctuations, suppose there were no other shocks,
how much of the business cycle would remain? We set up a model that we thought would answer that question.
The key about that model is that it contains an explicit description of people's dynamic decision problems.
It also contains a description of businesses.
There are millions of people in the model,
thousands of businesses and they define the technology for converting capital and labour inputs into outputs.
It's explicit about people's budget constraints, as a result the aggregate resource constraint etc.
Of course you may not having been back there in the late '70s let's say-
you may not realize that this was quite different from what I usually call a system of equations approach.
Most of macroeconomics at the time was in a form of-well,
you just formulated a consumption function, investment function, labour supply equation, labour demand equation etc.
And then at least many people had great faith in statistical methods to quantify the parameters of those equations.
Well, Robert Lucas changed all of that with his critique, at least for the purpose of evaluation of policy.
And so it was inevitable that something would come instead.
And I guess Prescott and I were lucky to be around the time when that movement started.
So, to me that's the key to have this explicit description of people.
Admittedly in the beginning simple descriptions partly for reasons-well,
it wasn't really necessary to have more detail for that particular question.
And also in those days computers were much slower than they are today.
Since then great strides have been made in expanding the set of questions that can be addressed.
Some people call this kind of theory Real Business Cycle Theory
because of the nature of the question addressed in that first paper.
But there's really no difference in type or model used for studying monetary phenomena, monetary policy.
And I've done some of that myself and that kind of paper I talked about two years ago in this particular room.
For questions of more-where the long run is more important, questions about the health of the social security system or...
the benefits or costs of immigration or other such questions...
These models have been expanded also to allow for life cycle behaviour, overlapping generations.
I regard Victor Rios Rull as one of the pioneers in that field.
And I had fun from the fact that he was my colleague at Carnegie Mellon the first three years of his career.
I saw how those models developed from models that took-well,
in the beginning they might take a day or two or a week to calculate the equilibrium.
So, I regard the longer run benefits of that work.
The long run benefit I think is that this is the way people have come to realize,
this is the way we need to set up models to address interesting questions.
Let me just talk a little bit about the key steps I see in writing a good paper.
One is: One should always be explicit about-or have a well thought out question to address.
Otherwise it's hard to get readers interested.
There's no standard model to be used for any question.
The model you select must be a model that you can regard as appropriate in light of the question you are addressing.
And so the models will look very different from one question to the next.
Sometimes some similarities, still a description of people's decision problems and the technology, the nation's technology etc.
These days we are using models also for looking at for example the interaction of the household sector and the business sector,
quite detailed models of decisions made in a family.
Very interesting work taking place.
One of the benefits of this set up is it's often quite transparent how to quantify the parameters of these models.
These models need to be calibrated, in order to give credible answers
and much of the effort taking place in calibrating such a model has to do with-it's kind of an inverse mapping.
So, if you calculate the equilibrium, the steady state of a typical model,
you get the steady state in the form of relations among variables.
And many of those relations among variables can be observed or measured quite easily in the data.
So, there's a mapping from the model to relations among variables that potentially are observable.
Calibration, most of the time is represented by that inverse mapping.
You go out and measure those things.
Often they are simple averages of things.
There could be a capital output ratio,
could be long run consumption related to GDP or average capital and labour income as a fraction of gross domestic income.
Things you can easily measure and then you map backwards to the parameter values.
Some people talk about deep structural parameters.
And sometimes I have the impression that there's kind of a magical number associated with each of them, no such thing.
There are some parameters that can be quite different.
Same parameter but across models may have different values depending on what else is in the model.
Just to illustrate that... Here's about the simplest model I can think of.
And I put it down only so I can talk about a couple of these parameters.
It has a description of people's preferences.
There's a utility function, a function of consumption leisure, discount factor, there's a resource constraint.
Consumption plus investment less than equal or equal here to the aggregate output.
That's the production function, and it's of course well known in national income-well,
that's gross domestic product in the model and that must equal gross domestic income which is a sum of capital and labour income.
There's a constraint on time.
And then there are the two dynamic relations between or among state and decision variables.
In the first the capital stock is the state variable, investment the decision variable.
And then there's a typical long motion for the technology level with a stochastic shock.
That in practice can be drawn from a normal distribution, from a random number generator in the model.
So, let me just mention two parameters.
One is the alpha, here on this coefficient on consumption, and 1-alpha on leisure.
This alpha is very much related to the following potentially observable thing.
Namely the fraction of total time on the average, devoted to market activity.
In other words: How large is N in relation to total time?
Once you plonk that down then there's a first order steady state relation.
You can work out and you can determine alpha.
There are a couple of other things in that steady state relation typically.
And so you won't always get-if you say that N is one third or 30% of total time, a typical value,
for some models you will get an alpha of 0.35 maybe and for some other model 0.28.
And that's a sense in which I say that there is no magical number associated with any of these parameters.
The point is either way you will get, you will satisfy the goal of making the model consistent
with something that you have observed, namely that N represents 30% of people's average time.
The other example that I can mention is the theta.
1 - theta in the Cobb-Douglas production function-much used production function.
Although for some questions you obviously want to disaggregate for example into structures and equipment on the capital side.
And into high skill and low skill workers on the labour side.
But this is the most simplified version of it.
And again the theta... It's an example of where typically there's only one thing it depends on.
And that is the fraction on the average of gross domestic income that is in a form of capital income
and of course 1 - theta, the fraction that's in the form of labour income.
So then, if you come up with a number for them, for example capital income is 36% of gross domestic income,
then typically theta will be 0.36.
If you have more decimals, more decimals than theta.
So, then of course there's the much used computational experiment to calculate outcomes.
The idea is that you put people in the environment,
you wish to put them to study the question you are interested in and you observe how they would behave in that environment.
In some cases there could be substantial challenges associated with that,
some cases you may discover that-well, for my question there's not really a methodology that exactly does what I want to do.
You may need to develop some new technology.
And that's something-I don't have much interest in technology, in methodology per se-
but it's something if you need to, you need to develop it.
And finally the presentation of findings,
and one thing that beginners often are a little nervous about is being upfront about anomalies.
That's a big mistake.
One definition of an anomaly is a discrepancy in the data relative to standard theory or maybe relative to your theory.
And if the model gives good answers to your questions but there's still some anomaly,
those often are the great research projects for the future.
So you want to emphasize them.
Now, the recent application.
This is a paper co-authored, it's getting close to being finished.
And in fact, my two co-authors or at least one of them I think will present it in Milano next week.
There's a big European conference.
So, it's joint with Espen Henriksen who is at the University of Oslo and Roman Sustek who is at the Bank of England.
They are both former students of mine at Carnegie Mellon University where I used to be before 2004.
And the paper is called "The High Correlations of Prices and of Interest Rates across Nations".
Now, what we discovered and I'll show you some pictures.
Here's a picture, suppose you detrend all of these variables,
you use either Hodrick-Prescott filter or I think in this case we used so-called Band-pass filter.
So, you take out the trend component
and you picture the cyclical component of real GDP on the left side and prices on the right-hand side.
And this is for a bunch of countries.
Eight countries in fact.
Now we had data for two of these eight countries only from 1970, for the rest starting in 1960.
These are Australia, Canada, Germany, Japan, UK and United States.
So these are credible countries.
And after 1970 we add Austria and France.
What these pictures indicate is-well, especially there is some synchronicity in output,
but the output picture is somewhat blurred.
The synchronicity to the naked eye I would say is substantially greater in the price level.
It's always best to check the actual correlations.
And this is a table of correlations across nations.
So, for GDP the average correlation across nations is 0.27.
And later, when we get to the price level and interest rates you may wonder if this would have been any different,
if they are driven by the Bretton Woods period which is included in this sample period starting in 1960.
And it turns out no difference.
So these are rather low correlations.
They are not particularly high across nations, when we look at real GDP.
For the nominal interest rate you get a mean correlation much higher, 0.57 versus 0.27.
And if we do the same for the price level, we get 0.52 versus again 0.27.
This may seem a little surprising, I mean these are that the synchronicity of nominal variables,
the price level and nominal interest rates would be higher than that of output.
You might think, well, domestically you do have some control over the nominal environment.
You would think you are less subject to external shocks than in the case of output and so we decided to investigate.
We figured if we get a handle on why this is, we will understand better how nominal variables or how monetary policy propagates.
And so the question we address is especially this relation among correlations.
Is this consistent with a baseline or a standard model?
Here's another way to look at it.
If we picture, make a dot for each of the combination of output correlations-I put correlations on the horizontal axis,
interest rate and price level correlations respectively by circles and blue dots.
The fact that most of them are above the forty-five degree line is an indication
that the nominal correlations are substantially higher than the output correlations.
I don't have time to give you much detail about the model economy.
We used a model that I helped co-author with David Backus and Patrick Kehoe, a model I forgot the exact title,
it appeared in the AER 1994, something involving the J-curve.
It's a two country model, symmetric, each country has its own production function, its own preferences,
but they are all symmetric but they produce different goods.
How these goods get combined to produce output of a consumption investment
is described in the form of a so-called Armington aggregator
which allows you to think about and quantify the elasticity of substitution between home produced and foreign produced goods.
And relate the magnitude of the elasticity to numbers people have estimated.
There's a whole book by John Whalley looking at elasticities between home produced and import goods
at different levels of aggregation.
That model was a purely real model and so we augmented that model with nominal assets, money and bonds,
and also monetary policy is described by Taylor rule which we argue is a standard,
I should have put that in quotation marks, parsimonious approximation of a central banks behaviour.
Here's a key thing I want to show you from the model because it's the key to our finding.
We find that the model is indeed consistent with the relation you saw between correlations.
It is consistent with much higher nominal interest rate and price level correlation than the output correlations.
And this is one of the fun things about doing this kind of work.
It's often hard to predict what's going to matter the most.
We had some conjections about why this would be,
what would be the key feature that would result either in the model doing well relative to the data or not so well.
And the surprising thing to us was these red numbers turned out to be crucial.
So this is a description of the technology levels in the two countries.
Z you saw that on a previous slide, the notation Z for the technology level and Z1 is technology level in country one,
Z2 in country two.
And what this slide says is that the technology level movement in the two countries they are interdependent in two ways.
And the sense in which they are Backus, Kehoe and I estimated the extent of this interdependence
using as the one country empirically United States on one hand
and the collection of major European countries as the other country.
The estimates weren't exactly symmetric and so we made them symmetric by choosing the symmetric matrix in this case
with the same eigenvalues as the actual estimated ones.
But the estimated ones weren't far from symmetric.
So the interpretation of these two numbers I would call them spillover effects.
They say that if there's a shock in one country, it spills over to the other country at the speed given by that 0.088.
There's also a second source of interdependence namely
that-contemporaneously there's a positive correlation between the two shocks
but that's not so important for our purpose.
So this turned out to be an important thing.
If we put these two numbers to zero and instead make the diagonal elements higher, this relation virtually disappears.
But in case you are a little suspicious about this high number,
obviously we did some sensitivity analysis
and we checked whether the relation among the correlations would still maintain, suppose you reduce it to 0.06 or 0.04 or 0.02.
It works even for small numbers, for small spillover effect.
So that's the key finding.
I saw a zero coming from Peter.
I guess I'm out of time.
What I can do is recommend strongly that you get hold of that paper and read it if this sounds interesting to you.
Thank you very much for listening to me.