On The Use of Rational Expectations

Lars Syll has a couple of posts (here and here) on rational expectations.  Of the various assumptions underlying microfounded macro this one is, for many heterodox economists, the most preposterous.

In my view, though, it is wrong to dismiss rational expectations out of hand.  I would not suggest it is embraced whole-heartedly with other concepts pushed aside if they don't fit neatly with it.  However, I do think it is important that economists appreciate the way that expectations shape results and in this respect, I think paying proper attention to the implications of rational expectations is an important discipline.

When constructing models, it's often necessary to say something about expectations.  In saying how people act in aggregate, we need to make some assumption about what their average expectations are.  This is particularly so when modelling financial markets.  The results we get from our model will then depend on the assumptions we have made.

In a sense, we have two choices when deciding how to model expectations.  We can either assume that people get it right or that they get it wrong.  Now, it's quite reasonable to suppose that people will almost invariably get it wrong.  The problem, though, is that it's not enough simply to say that people will get it wrong.  Unfortunately, if we don't want to use rational expectations, we have to make a further assumption about the precise manner in which people will get it wrong.  How confident can we be that this further assumption is the right one?

I think it is legitimate to make assumptions that involve people making systematic expectations error.  More than that, I think we have to make such assumptions to understand certain behavioural patterns that occur in the real world.  It is quite clear that people do make expectations errors and consequences follow from this.  These are things we need to be able to explain as economists and they cannot be explained by appealing to rational expectations.

However, what I think is really important is that we understand the extent to which our results depend on our assumptions about expectations formation.  It may be appropriate to assume that people make systematic errors, but we should still have some idea of how the model would perform under rational expectations.   This will inform us on the extent to which our result depends on these assumptions.  This is important, as any assumption we make about expectations is unlikely to be reliable.

Of course incorporation of rational expectations into models is not straightforward.  In many cases, expectations can be self-fulfilling, so use of that assumption can lead to indeterminate solutions.  Furthermore, adapting the structure and other assumptions of the model simply so that it can solved whilst preserving the preferred expectations theory, is not really a helpful approach.  We might decide that rational expectations just doesn't work in our model.  But that's not a good reason to abandon the model.

So we should always ask ourselves how the model would perform under rational expectations.  If we conclude then that our result depends purely on an expectation error, that doesn't invalidate the result.  But it is something we need to know and understand.

UK Housing - A Look at Some Ratios

Steve Keen has an article in Business Spectator on housing bubbles, a topic he has written about extensively before.  Comparing various countries, he describes the UK as having the "Big Daddy" of all possible housing bubbles.  Certainly, his graph is impressive, showing a fourfold rise in real house prices in the UK since the 1960s.

The graph below is perhaps less impressive.  This shows the average house price in the UK divided by disposable income per head.  Also shown is the ratio of household debt to disposable income.

 

There are various interesting things here.  First, the ratio of house prices to income, whilst higher than average, is not greatly so.  The last figure in the graph is 112% of the average for the period.  It is clear though that this depends on the period being observed.  If we were to look at the figures only from the early 90s onwards, for example, current levels would look much higher.  Taking the data back to 1955 (the earliest figures I could find) wouldn't change the picture much.  The current level is 115% of the average over that longer period.

Another thing we can see in the graph is the massive rise in household secured debt over this period, from around 20% of disposable income to a peak of around 130%.  Although the rate of change of this ratio has varied over the period, it is only in the last few years that it has shown any material decline.  What is also interesting is that the three peaks in relative house prices all come after a period of relatively strong growth in the debt ratio (less marked in the first instance).

There are good theoretical reasons for expecting a relationship between the level of secured debt (most of which is mortgage debt) and house prices.  What is less clear is the causal nature of that relationship.  It could be argued that both the rise in house prices and the increase in debt are the result of an increased demand for housing, which is itself caused by other factors.  Those factors might be demographic, interest rate related, speculative or something else.

An alternative analysis would see the increase in debt as itself part of the explanation for the increase in house prices.  Under this approach, there would be some level of latent demand that is constrained by the lack of finance.  As more mortgage debt becomes available, perhaps as a result of developments in the finance industry, this demand becomes effective.  Certainly, the latter two periods of growth in debt ratios have coincided with significant financial deregulation and innovation.

As always, the true answer is probably a mix of both.  However, my own view is that the latter effect is probably the more important.  It might reasonably be questioned whether a demand for debt levels over 100% of income has really been lying latent since the 1960s.  However, I think it is quite possible that as higher debt to income levels become the norm, they set a new benchmark.  This generates a new layer of latent demand.  Thus, the standard debt ratio slowly grows over time (although of course it cannot grow forever).

As a further piece of analysis, I looked at the net equity in housing relative to income.  This is shown in the graph below.  This is based on the graph for house prices, but I have subtracted out the average level of secured debt per property. 

 

The shape of line is fairly similar to that for house prices but with less slope, reflecting the rising debt.  From this graph, the current level of this ratio is pretty much equal to its average value for the period.  Why might this measure be relevant?  Well, one possible factor is that people in the UK may regard the net equity investment in their home as part of their core lifetime savings.  They accumulate wealth during their lifetime and feel more secure investing that wealth in owning their own home rather than in financial investments.  Under this analysis, relative returns on different assets are less important.

On this basis, the ratio of net equity to income simply reflects a normal lifecycle of saving.  If additional debt funding is provided to the housing market, the net equity investment stays the same.  The total investment in the market therefore increases, which will result in increased house prices.

Again, whilst I don't think this is a complete description, I think there is an element of truth in this as a description of the UK housing market.

So, does this mean the UK has currently got a housing bubble?  I think the answer depends mainly on what happens with debt, which may depend a lot on what happens with interest rates.  If interest rates remain relatively low going forward, then it is quite possible that the debt to income ratio can remain at a high level for quite a long time.  If that can happen, the UK could quite easily avoid a significant fall in real house prices.

None of this however addresses the inequitable ownership of property in the UK, which is itself symptomatic of these trends.  This is something I want to look at in later posts.

Sources: ONS, Bank of England, Nationwide, DCLG, own calculations.

Must We Choose a Paradigm?

Simon Wren-Lewis makes a plea for a more open attitude to behavioural economics in macro.  Overall, Wren-Lewis supports the idea that macro models should be microfounded, but believes that the micro foundations used are outdated and ignore inconvenient facts about how people really behave.

To me, this is merely symptomatic of a more general problem in economics.  This is the tendency to focus entirely on one particular paradigm or modelling style, and reject any approach that might contradict it.  This seems to me a dangerous attitude.

Before I go any further I should say something about my own approach.  Those familiar with this blog will know that I often resort to models to develop my own understanding and to illustrate what I think are interesting points.  They will also probably have noticed that many of my models are of a particular type, with a heavy focus on social accounting and institutional structure. 

However, the reason for this is not that I think this is the only way to model, but is rather more basic.  The reason for the absence of New 'Keynesian' style modelling in this blog is mainly that I'm not very good at it.  I never formally studied it and although I have enough of a grasp to be able to understand other people's work on this, I do not feel fluent enough to present ideas within that framework myself.  So I stick to what I know.

But that does not mean that I think DSGE modelling is wrong.  Actually, I don't think any modelling is right or wrong.  As I have said before, all any model does is illustrate how certain conclusions flow from certain assumptions.  It never actually tell us how things really are, because the assumptions always represent a gross simplification of the real world. 

This means, I think, that there is a lot to be learned from taking into account the results of different models.  Some models may be better than others, but all have their weaknesses.  Using different approaches helps us identify those weaknesses and understand how to interpret our results accordingly.

So what happens if two different models give us seemingly contradictory results?  Does this not mean we have to choose to simply reject one of them?  Absolutely not.  In fact, I think this provides us with an excellent opportunity to identify how crucial certain assumptions are and to give us greater insight into the strengths and limitations of both approaches.