The debate that
continues about whether a Phillips curve still exists partly reflects
the situation in various countries where unemployment has fallen to
levels that had previously led to rising inflation but this time wage
inflation seems pretty static. In all probability this reflects two
things: the existence of hidden unemployment, and that the NAIRU has
fallen. See Bell
and Blanchflower on both for the UK.
The idea that the
NAIRU can move gradually over time leads many to argue that the
Phillips curve itself becomes suspect. In this post
I tried to argue this is a mistake. It is also a mistake to think
that estimating the position of the NAIRU is a mugs game. It is what
central banks have to do if they take a structural approach to
modelling inflation (and what other reasonable approaches are
there?). Which raises the question as to why analysis of how the
NAIRU moves is not a more prominent part of macro.
The following
account may be way off, but I want to set it down because I am not
aware of seeing it outlined elsewhere. I want to start with my
account
of why modern macro left the financial sector out of their models
before the crisis. To cut a long story short, a focus on business
cycle dynamics meant that medium term shifts in the relationship
between consumption and income were largely ignored. Those who did
study these shifts convincingly related them to changes in financial
sector behaviour. Had more attention been paid to this, we might have
seen much more analysis and more understanding of finance to real
linkages.
Could the same story
be told about the NAIRU? As with medium term trends in consumption,
there is a literature
on medium term movements in the NAIRU (or structural unemployment),
but it does not tend to get into the top journals. One of the
reasons, as with consumption, is that such analysis tends to be what
modern macroeconomists would call ad hoc: it uses lots of
theoretical ideas, none of which are carefully microfounded within
the same paper. That is not a choice by those who do this kind of
empirical work, but a necessity.
Much the same could
apply to other key macro aggregates like investment. When economists
ask whether investment is currently unusually high or low, they
typically draw graphs and calculate trends and averages. We should be
able to do much better than that. We should instead be looking at the equation that best captures the past 30 odd years of
investment data, and asking whether it currently over or under
predicts. The same is true for equilibrium
exchange rates.
It was not just the
New Classical Counter Revolution in macro that led to this
downgrading of what we might call structural time series analysis of
key macro relationships. Equally responsible was Sims famous paper
1980 ‘Macroeconomics and Reality’, that attacked the type of
identification restrictions used in time series analysis and which
proposed instead VAR methods. This perfect storm relegated the time
series analysis that had been the bread and butter of macroeconomics
to the minor journals.
I do not think it is
too grandiose to claim that as a consequence macroeconomics gave up
on trying to explain recent macroeconomic history: what could be
called the medium term behaviour of macroeconomic aggregates, or why
the economy did what it did over the last 30 or 40 years. Macro
focused on the details of how business cycles worked, instead of how
business cycles linked together.
Leading
macroeconomists involved in policy see the same gaps, but express
this dissatisfaction in a different way (with the important exception
of Olivier Blanchard). For example John Williams, who has just been
appointed
to run the New York Fed, calls here
for the next generation of DSGE models to focus on three areas. First
they need to have a greater focus on modelling the labour market and
the degree of slack, which I think amounts to the same thing as how
the NAIRU changes over time. Second, he talks about a greater focus
on medium- or long- run developments to both the ‘supply’ and
‘demand’ sides of the economy. The third of course involves
incorporating the financial sector.
Perhaps one day DSGE
models will do all this, although I suspect the macroeconomy is so
complex that there will always be important gaps in what can be
microfounded. But if it does happen, it will not come anytime soon.
It is time that macroeconomics revisited the decisions it made around
1980, and realise that the deficiencies with traditional time series
analysis that it highlighted were not as great as future generations
have subsequently imagined. Macroeconomics needs to start trying to
explain recent macroeconomic history once again.
