The countries love to build models and, given the topic and given some data, a bit of theory, they will build a model.
It could be a model of an industry or a region of some country, or maybe a whole country.
And we’ve had country models now for at least 50 years,
they are getting increasingly more complex, more sophisticated and hopefully better.
For the last decade or so it has been realised that a country model is very limited in its value.
Because what happens in one country is affected by what happens in other countries.
Partly because of the growth of trade in the world economy, you have to be aware of your success with the trading partners.
Trade is one of these things that the human race has always realised the benefits of.
In every grave site that people dig around, they find evidence of trade from the very earliest days.
I mean, from a piece of stone or something they find there, or even precious metals, flint,
they find evidence of trade, because these things are not found in the area of the grave.
And trade, as you all know, has been increasing dramatically over the last decade.
The growth of India and China as major trade partners with the more developed countries,
or the then more developed countries, has made trade hugely more important now than it was say 20 years ago.
And so there’s no point in building a single country model, you have to build a model that is a global model.
It becomes quite a large task to build a model of the global economy.
Let’s think about that for a second.
The world contains about six billion individuals divided into about one and a half billion families.
Each family would have at least one decision maker, probably more than one, but let’s just say one.
There are also many millions of other decision makers, government units, corporations and so forth.
There’s at least two billion decision makers around the world.
A model must encompass all the decisions made by these individuals or groups,
because they are not all reaching the same decision and not for the same reason.
They are basing their decision on different information sets and therefore they are not all in sync with each other.
So we are faced with a very huge task of building a model that captures
what is going on amongst all these different decision makers.
Now, what we could do to simplify everything.
We could build a single and simple model for the whole world economy just using aggregate variables.
So, for example, we could find an aggregate global GDP data.
In fact there is such a series, people have put that together already.
The value is another question.
So you put the GDP of India and China and the US and Germany and everybody else,
all into one series and call that the global GDP.
You could also get a global production series or a global consumption series.
There are various other series you need to build a major macro model.
Unemployment is more difficult because the definitions of unemployment are different in different countries.
And so we'd have to have some way of merging that.
In the US, if you work one hour a month, you are employed, whereas that wouldn't be true in other economies.
We have to find some way to get around that, we could always do that.
We already have international markets for interest rates, so that’s okay, and various commodities, such as oil and metals.
The various silver and gold and so forth, scrap steel.
There are markets for those, so that’s part of the whole global thing.
We could put a model together of all these global aggregate things and build a simple model.
It’s quite possible that such a model exists somewhere, I’ve never seen it.
It wouldn't be that difficult to do.
People wouldn’t like it very much, it’s too clunky, too unsubtle as a model.
But it would be possible.
With the construction of a world central bank, which people are talking about, this is a model that would become quite relevant.
What's the other extreme?
To build several economy models for each country in the world.
That’s a lot of countries.
Right now, in the United Nations there are 192 members.
192 separate country models.
Some of those countries are pretty small.
San Marino is a little dot in the middle of Italy, a little mountain top.
It only has a few hundred people living there.
It’s a member of the United Nations, so it will count as a country.
Vatican is actually not a member of the United Nations, so it doesn’t count as a country.
But there are lots of quite small countries in that 192.
Some of these countries will not have much data.
Perhaps have none at all.
And you can’t build a model without some good quality data.
So you can’t build a model for every country.
It can be down to the question of how many countries could we build models for.
And the question is:
Is it really worthwhile building models for some very tiny countries
which have very little impact on the rest of the world, compared to some of the big countries.
And so although we could do an every-country model, or every country that has a data,
I haven’t seen this model, I doubt it exists.
There is actually quite an interesting approximation to it called Project LINK.
Project LINK is a model that has existed now for 40 or 50 years, started off by Larry Klein, Nobel Prize winner from Penn.
It consists now of about 60 countries.
So each country has its own model, they are all of similar construction form, not identical.
They don’t have the same sort of data everywhere.
But they all are supposed to be rather similar in form and they are built by teams of expert econometricians,
mostly based on that country but some other people would visit from places that have more econometricians hanging around.
They’ll go and help you build the model and maintain the model.
So Project LINK now consists of about 60 models for different countries
and they would churn out forecasts for things for each country and they are all linked together by trade flows.
That’s an example of a global model which has been existing now for at least 20 years, maybe a bit more.
They are a little bit - I have to be careful how I phrase this –
they are not very outcoming by what specifications they use for the models.
They don’t publish the models at all.
Occasionally one slips out a bit, but they seem to be quite dynamic, fairly modern in structure.
Not sure if they all have the same model, they have similar models.
Not clear if they are the most up to date of all possible specifications.
But they are okay, they use some error correction forms and they are certainly linear and dynamic.
There’s a centre in Toronto which runs the whole LINK process and there’s a website for LINK,
but it doesn’t say much about what the actual models are.
They also have an annual meeting, in fact two, I’ve been to one of them.
It’s very interesting, they don’t discuss individual models.
They have posted many forecasts but they don’t have any confidence around the forecasts,
which makes evaluation of the forecasts very difficult.
I may mention that later on.
For example, in May 2005, they gave forecasts for oil prices for say 2005 and 2006.
They said that oil price in 2006 would be 37 dollars, but with no confidence intervals.
In fact, in 2006 the oil price was 78 dollars.
Whether that forecast was good or bad we don’t know, because we don’t know the confidence interval.
If the confidence interval was only a few dollars, then it was a bad forecast,
if it was 100 dollars, it’s quite a reasonable forecast.
Point forecast without a confidence interval is not easy to evaluate.
There’s very little knowledge about Project LINK, even though it’s a very big project.
It must employ a lot of econometricians.
I mean it’s 60 countries, each has its own model.
It must be at least 200 or so econometricians around the world working on this project.
Yet it’s not very well known as an area.
What's the alternative to LINK?
What one could do is to group the countries in various sensible ways and then model the groups.
Let’s think about that for a second.
Suppose we look at the say eight major countries:
US, UK, Germany, Japan, France, Italy, Spain and Canada.
Together they make up 46% of the world’s GDP, 12% of the world population and 47% of the world exports.
So eight countries by themselves will give you nearly half of the world GDP and exports, but not much of the population.
If you expand that up to the 29 advanced countries list,
you can get that up to about 55 % of the GDP, 15% of the world population and 70% of the world exports.
So expanding them over countries doesn’t buy you a lot in terms of how much more of the world you capture.
If you add China and India to that, you then get 29% plus China and India,
that’s about 74% of world GDP, 56% of the world population and 80% of world exports.
So if you build a model based on 31 countries, you capture most of the world’s GDP,
most of the world exports and about half the world population.
And for economic purposes, that’s probably a reasonable group of countries to look at.
The rest of the world is important population-wise, but not economically in terms of trade.
And so people could have built models based on that type of approach,
you just have a group of countries and build models for that.
So some global models use just a number of single countries plus the 10 I just mentioned plus groups of other countries,
might be called Western Europe, Eastern Europe including Russia, Southeast Asia and so forth, South America.
One of the problems is that the quality and the quantity of the data available for these different regions vary a lot
and it’s not just a matter of what type of country it is,
it depends on whether they were always very interested or careful in gathering data, information.
And so some parts of those countries didn’t think about gathering much data until at least 20 years ago.
There isn’t much early data.
In other countries it was expensive to gather a lot of data, and there wasn’t much effort expended on it.
That’s true of say Russia and China until recently.
India was different because India had a lot of Brits there, British people who were always interested in gathering data.
So any ex-British colony has reasonable data, that’s the only good thing about it, but it’s certainly something that is a fact.
So the quantity of data and the quality of data in different countries varies a lot.
That very much determines what you can do in terms of building models in different countries.
So when you are building a model based on lots of countries, what choice of country you use
and how you group them would depend on the objectives of the model as well as the resources available for model construction.
But they are particularly concerned with the quantity and quality of the economic data that’s available.
If the purpose of the model is to represent those many economies that will naturally need a very comprehensive model.
If you have a lot of economies in your model, then you need a lot of work.
Let me go through a few of the models that are out there to explain the differences that exist now.
There’s a guy called Ray Fair at Yale, who has been producing macromodels of good quality now for several decades.
His website has a lot of information about the models, he’s got a couple of books on them too.
He’s one of these people who has been very open about what's in the models,
what the parameters look like, how they are estimated and so forth.
It’s a very excellent approach to presenting models.
And in his book he has really two major models, one for the United States and one for the rest of the world.
The rest of the world model involves now 45 countries and 30 of those have structural equations.
30 of the models have quarterly data, most of the others just use annual data.
With annual data you can’t get much dynamics in the model, because they can’t be renewed quicker than every year,
so really every quarter, perhaps every month.
You really need quarterly data to do a good job.
He does talk about what sort of form model he has, they are often quite complicated,
there are often changes of logs, of variables.
As these are published, you can look at them all very well.
Not only that, his whole model is on his website.
So you can look it up on the web, you are even allowed, if you want to, to change some of the coefficients.
And seeing how that would effect the forecast being made by his model.
I mean, he has a very open approach to the whole modelling process.
Unlike almost every other modeller that can be mentioned.
He also tests his models in many sensible ways, including forecasting, which I want to talk about later.
He also runs some interesting party simulations about what would happen to the US com
if the Federal Reserve increased interest rates substantially, for example.
That would bring me back to evaluation later on, because we can evaluate forecasts.
We can’t evaluate these policy tests because Fair never did that.
And so we have to only evaluate things which actually happened in the economy, not things that might have happened.
It’s interesting, I asked the question what would the model say if the Fed did something.
But we can never check that.
So the Fairmodel are interesting group models.
A modern version of that I call the Pesaran model, Professor Hashem Pesaran at Cambridge worked on this model, and his workers.
Considering a model called regional independences, using a global error correcting macroeconometric model.
It’s based on the model too.
This model is now being used by the European Community as a sort of official econometrics model for the EC
and also the connection with the EC and the rest of the world.
So it’s becoming a model that is being used by real people, real decision makers in important ways.
So that’s a model that I think is now probably the best model that’s available.
You can see it in the old form - the most recent form is not available yet,
but will become available soon - and it has very high quality.
It’s based on the error correction model so it’s mostly linear, with some non-linear components.
But it’s very dynamic, which is what I prefer.
Different sort of model entirely is the so-called computable general equilibrium models,
which are based entirely on theory and based on the idea that if the economy is in equilibrium now
and it’s bummed out of equilibrium for some reason,
the theory would tell you the route that the economy will take to get back to equilibrium.
The route that every variable in the economy would take to get back to equilibrium.
So it makes a forecast that after a big shock hits an economy how the economy will behave.
That’s a very interesting model.
Based entirely on theory there is some data involved in it, but it’s basically quite a good model.
It doesn’t tell you how long the model will take to get back to equilibrium.
It says it will go back to equilibrium and this is the path it will take.
There’s now a nice model called G-Cubed, run by a guy called Warwick McKibbin, Professor McKibbin, an Australian.
That is a private model, it’s a model that you can buy if you care to.
It will cost you several thousand dollars to look at.
That’s one of the problem with many of these global models now, that they are becoming commercial.
Therefore they are not very useful for most people.
There are a number of those I could mention that people are building models,
they may or may not be good models, we never see them.
We get adverts for them, they describe how they are done, but we never see the outputs, we don’t know how to compare them.
There's also a science based model, made more complex.
There are six linked sectors:
population, pollution, geographic space, agriculture, natural resources and capital investment.
It seems to be largely deterministic, it’s a set of written down equations,
there’s very little estimation being done, based on data.
And it has dynamics, and so again you get the process going and you observe what's going to happen.
Now, the original Club of Rome model in 1974 got a lot of publicity.
It was predicting that by 2010 the whole world would start to collapse.
Essentially pollution would be so bad by now that we couldn’t produce any more food
and therefore we would get starvation and industry would start clogging up.
That was met with such derision by most people that the government went back and re-estimated their model
and put some extra facts in, and then they actually changed the date of decline to 2014.
We have a few years yet before the collapse, according to this model.
The model had all kinds of other strange aspects, economically, it had a very peculiar production function, for example.
If you doubled physical input and doubled labour in production function, you quadrupled output.
That’s not the way the production function normally works.
If you double all input, you double output.
But they had a different function.
So anyway their model was not viewed very sensible, got a lot of publicity.
They are now working on a new formula model and all we know is that they are doing it,
nobody knows, no discussion, there’s nothing public about it.
The website is almost empty and all we can tell is it’s going to be probably similar to the old one but hopefully much better.
So a lot of these other approaches around, which we can’t often get at.
Maybe about 10 or 15 other models we can’t get at.
The fact that a model has been estimated, it even has firm foundations, doesn’t mean it’s any good.
We have to always evaluate our models before we know how good they are.
That’s true anywhere in econometrics.
Anywhere in forecasting.
So how do you evaluate a global model?
It’s a big task, these models are often enormous.
Well, people always say forecasting is the first step.
Do these models forecast?
And the answer is:
Yes, they forecast all over the place.
But are the forecasts any good?
There’s lots of ways to evaluate the forecast.
That would take another whole lecture on the forecast evaluation.
But the question really is:
Do we expect the global model to forecast well for every country and for every variable in the economy,
better than some other model?
That’s an enormous task, I would think very unlikely.
And the few evaluations that have occurred, say with the Pesaran model,
is that it does forecast quite well in several economies with several variables,
and it would be a very simple forecasting model around them all.
That’s saying it is doing a pretty good job, but it doesn’t say it’s the best forecasting model, it’s probably not.
You are asking too much of a model to be good at everything, everywhere, every economic variable.
You have to be much more specific, what's the purpose of the model.
These models are not being built for forecasting, they can be used for forecasting,
they are being used for forecasting, they are producing forecasts.
But they are really devised for policy.
They are most useful for questions like if something happened to the world economy,
if there was a sudden shock to the world economy, what would be the effect with the various economies?
So, for example, suppose oil prices suddenly went up by 25%.
How would the various economies change with that change in oil prices?
Every economy, every model has oil prices now in as an exogenous variable.
Okay, so oil prices went up suddenly, how will the model forecast oil prices, affect oil prices?
Well, that’s happened recently.
We could go back and look at what the model is saying, 10, 15 or 20 months ago,
about what the model would predict would happen if oil prices went up substantially
to various economies and to various variables.
We can now see how or what did happen to the economies because that’s now in the past.
So we could actually use that to see how well the models were doing on a particular shock to the world economy.
We could ask for other things, what would happen if some major economy collapsed?
I suppose Japan suddenly had a very bad problem, or China had a very bad economic problem, or US is having a very bad problem.
What would the effect be on other economies?
These global models are supposed to be able to tell you the forecast, the side effects,
these turn-on effects from one economy to another economy, which other models can’t possibly do.
Because there’ll be effects on trade and through each other the effects will keep going round and round
and eventually work themselves out to where will the major effects be.
Will the EC be more affected by the big shock to the US economy or less affected?
And so forth.
Well, we can run quasi-simulations with these global models
and they will then, hopefully, enable us to have some idea of the uses of the model.
Some of that has been done, mostly with the Pesaran model and probably with the Fair model, I’ve not seen that on the web.
And possibly the LINK, but I’ve not seen that either.
So most of these models which could be doing interesting evaluation attempts are not occurring.
At least they are not being shown publicly.
We could think about forecasting trade links.
A model that is based on trade should be able to forecast trade links.
And how they’ll be changing as an economy has some shock to it.
So if India has some economic shock and is trading with the EC and the US, how will those trade links be affected by India?
And then how will that pass on through the US and so on onto other countries?
Interesting China and India don’t do much trading, so they wouldn’t affect each other very much.
It would always be indirect effects but not direct effects.
So these models should be able to tell us the effects on trade of shocks to one economy to another.
Which you can’t get any other way other than by a big model.
The models have potential to tell us a lot about the world if they are any good,
but they are so big and so difficult to evaluate, you have very little idea whether these models work or not.
And so the next decade or so has to be in terms of looking at the models,
asking the question ‘do they work very well?’ in the situation we’ve seen, where the world economy has done something.
Did the model forecast that was going to happen, did it forecast what the effects of that happening and so on.
And we can then have models.
That will occur, it hasn’t happened yet.
What we are trying to do with evaluation is not to find the best model.
Historically we used to always think of, when we had a lot of different models,
we would try and get through and throw all of them out except one.
That would be the best model and that’s the one we’ll stick to.
But thinking about model evaluation these days is rather different.
Not just here but in models generally.
We are inclined to want us to throw out many of the models, but keep a group of good models, interesting models.
A decision maker would rather have forecasts from several different models than a forecast from one model.
Even though the one model is the best model.
With several different models, you get forecasts from different viewpoints
and points that make us consider those different viewpoints through those forecasts.
We can combine the forecasts, if we want, and give the weights of different models,
but even better is to give the individual forecast, like thick and thin modelling.
Thin modelling is when you use a single model,
thick modelling is where you choose a group of good models and keep several models.
There’s now a tendency in places like the Federal Reserve Bank to move towards thick modelling
and not just choose the best model but to choose several.
Now, how we choose the best group to keep is very unclear.
It depends on the decision maker.
So we may want to throw out some very bad models but in forecasting, in portfolio theory,
say, we don’t necessarily want to throw out the worst behaving share,
because its combination with the other ones can actually produce occasionally good portfolio.
So it’s not quite clear, we know what criteria to use, of when to throw out models or not.
They are very expensive to things to hold around, keep around, so you don’t want to keep all the models.
It would be nice to throw some of them out and keep some of the others.
So the present state of the art is that we want to evaluate lots of models.
And evaluate them in terms of their relevance jointly to the ultimate object, which is to provide decision makers,
wherever they might be, in different areas,
with forecasts or notes about the usefulness of certain aspects of the model, for instance policy.
And then provide those just for a few good models and not all of them.
I’m going to finish a bit early, so if there are any questions, you are welcome to ask them.
Thank you.