But that is all in the past. What about the outlook for the
remainder of 2019? Obviously Brexit remains the elephant in the room as
far as the UK is concerned, so it is impossible to be precise about what is
likely to happen. In terms of what the evidence tells us so far, we know that
business fixed investment fell in each of the four quarters of 2018, and
in the second half the pace was particularly rapid with spending down 2.6% in
real terms in Q4 relative to Q2. By end-2018 the volume of activity was thus
the lowest since Q3 2015, and the second lowest quarter in four years. It is
possible that it will rebound in the absence of a no-deal Brexit but latest CBI
survey evidence continues to suggest that companies are likely to cut capital
investment over the next twelve months.
However, declining business investment is not necessarily a
good indicator of what will happen to overall GDP. In 14 of the last 53
years business investment has actually fallen at the same time as GDP has
expanded. For four consecutive years between 2001 and 2005, when GDP growth
averaged 2.7% per annum, business investment declined at an average annual rate
of 2.8%. It is thus illustrative to look at the information content in other
data to see what it tells us. In doing so, I have relied on the literature
on qualitative choice models which tries to find leading indicators to predict
recession probabilities (see this New York Fed paper for insight into
how such models have been applied in the context of the US).
In applying the analysis to the UK, the object of the
exercise is to find indicators which have decent predictive power. I finally
opted for the CBI’s business optimism index and the Conference Board’s leading
indicator, which is in turn comprised of eight variables (order books, expected output, consumer confidence, bond prices, equity
prices (All Shares), new orders, productivity and corporate profits). Although
the leading indicator does contain financial variables, equities have a weight
of less than 4% so I added a series for real equity prices (using the consumer
spending deflator as the relevant price index) to specifically account for the
fact that markets often spot downturns in the economic cycle more quickly than
the published economic data.
As noted above this methodology uses models of qualitative
choice. Such techniques are used to model outcomes where the dependent
variable takes a binary 0,1 value depending on the contingent state. In this
case the dependent variable is the year-on-year rate of real GDP growth which
takes the value 1 when it falls into negative territory and 0 otherwise. Thus
what the model tries to do is assess the likelihood that annual growth will
turn negative [1]. Based on data back to 1973, we have 184
quarters of data, and on 25 occasions GDP growth turned negative. Using a basic
probit model, I estimated a relationship between lagged
values of the three explanatory variables and the binary dependent variable to
give an assessment of recession probabilities six months ahead.
As the chart shows, on the four occasions since 1973 that
growth has gone into negative territory, the model has predicted this six
months ahead of time with an accuracy rate of at least 90%. The model suggests
that on the basis of current data there is a probability of around 33% that GDP
growth will turn negative by mid-2019. In order for that to happen by Q3 would
require a sluggish Q1 growth rate and modest declines in Q2 and Q3 (i.e. a
technical recession). Absent a Brexit-related collapse, this would appear to be
a stretch. Thus the model likelihood of around one-third appears
reasonable – an unlikely, but not impossible, scenario.
In contrast to conventional forecasting techniques, we
do not attempt to quantify the rate of GDP growth. But the probabilistic
approach outlined here gives a sense of the risks surrounding the outlook and
thus some steer on how much preparation may be required to offset the
worst-case outcomes. Obviously, the analysis is based on the information
content in current data and is subject to changes resulting from random shocks.
Brexit could thus significantly change the picture, but that is a subject
for another day.
[1] Strictly speaking we ought to be looking at quarterly GDP growth
to define those periods where there are two consecutive quarterly declines, but
there is such a lot of noise in the quarterly data that the equations do not
fit the data particularly well.
