The Euro-area recession and
nowcasting GDP growth using
statistical models
Gian Luigi Mazzi (Eurostat), James Mitchell (NIESR)
Gaetana Montana (Eurostat), Kostas Mouratidis
(Sheffield University) & Martin Weale (NIESR)
Scheveningen, 15 December 2009
National Institute
of Economic and
Social Research
Purpose
• To produce estimates of quarterly GDP
growth in the Euro-area faster than
Eurostat’s Flash estimate at 45 days
• Produce nowcasts using statistical models
– Exploit information on indicator variables
• Large number of potential indicators
– Quantitative (“hard”)
– Qualitative (“soft”)
• Soft data tend to be published ahead of hard
data
Focus
• Assess the ability of some widely used statistical
models to anticipate the recent recession in the
EA, and then adapt to it
• Nowcasts are produced at 0 and 15 days after
the end of the quarter
• Nowcasts can always be produced more quickly
by exploiting less hard information, but we might
expect the quality of the nowcasts to deteriorate
as a result
• We identify what if any indicator variables were
most helpful in anticipating the recession
Methodology
• Use of real-time (vintage) data
• Use out-of-sample simulations
• Focus on models’ changing relative and
absolute performance
Indicator variables
• Monthly and quarterly: allow for the staggered
release of monthly data within the quarter
• Early estimates of GDP, whenever available
• Hard data:
– Industrial Production; available monthly at 45 days
– (Deflated retail trade data)
• Soft data
– DG-ECFIN’s Business and Consumer Surveys
– IFO survey
• Financial data: interest rates; the yield curve
• National indicators can be considered too
Modelling approach
• Regression-based nowcasts
– Estimate many models and select the
preferred model automatically using the BIC
– Monthly “bridge equations” used to exploit
within quarter monthly information
• Combination nowcasts
– ‘Integrate out’ model uncertainty, rather than
select the best model which may not be stable
• Factor-based nowcasts
– Small versus large (quarterly) information set
Regression-based nowcasts
p
yt  c    i yt  i 
i 1
p
k
  ij xt  i , j  ut ; (t  1,..., T )
(1)
i  0 j 1
yt is the log of the dependent variable (quarterly GDP growth in our
application), xt , j is the j-th (quarterly) indicator variable (j=1,…,k) in logs
where
• Variables differenced until stationary
• All possible combinations of variables and lags
• Restrict the number of indicators and lags to 3
(parsimony)
• Automatically select for each group of models
(containing respectively 1,2, or 3 indicators) the
best performing one using the BIC
Combination nowcasts
• Focus on equal weights
• Weighted (BMA) variants did not do any
better in this application
Factor nowcasts
• Extract principal components from the set
of (quarterly) indicators and use these to
nowcast quarterly GDP growth
Benchmark nowcasts
• Ability to beat the benchmark, systematically
over time, suggests that the model is of “use”
• Consider an AR(1) and a random walk
– Both use no within-quarter information
• Estimate using previous, as well as the most
recent, vintage of GDP data
– Data revisions may not be mean zero and
maybe predictable
Out-of-sample simulations
•
•
•
•
Use Eurostat’s real-time database
Compute nowcasts at t+0 and t+15 days
Compute recursively from 2003q2-2009q2
Compare accuracy against Eurostat’s Flash
GDP estimate (t+45) and the “final” GDP
growth estimates (as of 13 Aug 2009)
• Distinguish accuracy (RMSE) of nowcasts
over different evaluation windows
– Important as models’ absolute and relative
performance varies
Pre-recession performance (RMSE):
2003q2-2007q4
Post-recession performance (RMSE):
2003q2-2009q2
Accuracy of the nowcasts
• The recession has led to a doubling of
models’ RMSE statistics
• But some models adjusted more quickly
than others to the dramatic decline in GDP
growth which began in 2008q2
– The relative performance of models changed
substantially with the recession
Euro-area GDP growth nowcasts
Changing performance
• Pre-recession an AR is “hard to beat”
– With regression, factor and combination models
performing similarly
– Slightly better nowcasts are produced at t+15,
with two months of within-quarter IP data known
– But this regression model is not selected by the
BIC
• Post-recession an AR is easier to beat
– Within quarter information more clearly helps
– Selection better than combination
– Preferred indicator(s) change over time
Soft data became more informative
• Prior to the recession it paid to wait for the release
of two months of within quarter industrial
production data
• Over the recessionary period it was best to ignore
this statistical evidence and construct nowcasts
zero-weighting the IP data
– Focus on soft data alone; thanks to their forwardlooking nature they detected the recession more quickly
• Means nowcasts more accurate at 0 than 15 days
• Factor methods are more robust to consideration
of IP data at t+15 and perform well at both t+0 and
t+15
Instability: relative performance of
indicator-based nowcasts against an AR(1)
The lines represent the squared forecast error of the nowcast of interest
against the squared forecast error from an AR(1) computed at 15 days
Conclusions
• Models’ performance changes over time,
in both absolute and relative terms
• The utility of constructing GDP nowcasts
using indicator variables increased over
the recessionary period
• The relative informational content of soft
(forward-looking) data also increased in
the recession. But this appears to be a
temporary change
Future work: in progress
• Focused on the point nowcasts – or the
‘central’ (conditional mean) predictions
from the statistical models
• But at times of uncertainty it is important to
move beyond ‘central’ forecasts
• We should assess the ability of these
models to predict the probability of a
recession and more generally examine
their density nowcasts