National Institute of Economic and Social Research 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

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 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 forward- looking 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