Forecasting and Policy Making (Paper) - Center for Financial Studies

The nowcasts are updated daily to make sure that the information content of the most recent data
realizations is taken into account.
Bańbura et al. (2010) demonstrate the computation and interpretation of nowcasts for euro area
GDP. They use a dataset of 26 variables. The data is grouped into a part corresponding to real
variables and into another part corresponding to nominal variables. The real variable block is further
split into hard data variables, survey variables and financial variables. They show how the nowcast
is revised over time as new data becomes available and how the different data blocks contribute to
these revisions over time. Their results show that even very sophisticated nowcasting methods may
still deliver fairly imprecise nowcasts or one-quarter backcast of GDP. One of the reasons is that
GDP data is revised substantially even after the first release. Policy makers need to take into account
that they have to rely on rather uncertain estimates of the current state of the economy. An overview
and illustration of recent nowcasting techniques is given by Giannone and Reichlin (2012). Finally,
Giannone et al. (2009) show how to link high frequency nowcasting frameworks with a quarterly
DSGE model in a coherent manner.
Forecasting with non-structural time series models
Simple non-structural time series models are used for forecasting because they yield forecasts that
are difficult to beat in terms of accuracy. The high accuracy of these forecasts has been recognized
as early as in 1972 in a comparison of forecasts from the large econometric model used by the Fed in
the 1970s (FMP: FRB-MIT-PENN model) and simple time series methods: Nelson (1972) compares
the accuracy of one-quarter-ahead forecasts of 14 macroeconomic variables obtained from the FMP
model and simple ARIMA models. The simple ARIMA models are more robust and yield more
accurate out-of-sample predictions for 9 out of the 14 projected variables including GNP growth. 28
The robustness and accuracy of simple time series forecasts has been confirmed in many other studies.
Simple time series methods impose no incredible restrictions on the underlying relationships among
variables (Sims, 1980).
A recent comparison of simple non-structural forecasting methods with more sophisticated struc-
tural approaches is the paper by Edge et al. (2010). They compare forecasts from the current version of
the macro-econometric model of the Federal Reserve, the FRB/US model, to forecasts from a DSGE
model, from vector autoregression models (VARs) and a simple AR(2) process. Regarding most
variables the forecasts from the AR(2) model are of similar accuracy as the forecasts from the two
structural models. For real GDP growth the DSGE model provides slightly but not significantly better
forecasts, while the FRB/US model performs somewhat worse. For inflation and the federal funds
rate the AR(2) model yields significantly better forecasts than the DSGE model and only slightly
less accurate forecasts than the FRB/US model. Edge et al. (2010) find that forecasts from univariate
methods are even more accurate than forecasts from multivariate methods such as VARs and Bayesian
28 These results are obtained by using actual realized data for future exogenous variables in the FMP model. Without this
perfect foresight approach the forecast accuracy of the FMP model would probably decrease further relative to the ARIMA
models.
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