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

continuation in negative territory, because they are linear models. The zero-lower bound on nominal
interest rates requires the incorporation of a nonlinear constraint in the time series models. We will
revisit this question in section 5.6. For 2010 and 2011 the interest rate is projected to increase. How-
ever, a forecaster who uses the estimated rule with FOMC forecasts from section 4 to project interest
rates based on longer-run forecasts would have expected that the Federal Reserve would keep interest
rates on this level a bit longer.
Achieving accurate forecasts for output growth and inflation is even more difficult, because these
time series are less persistent. Neither model predicts the large recession following the global financial
crisis. The output growth forecasts are strongly mean reverting and thus not suitable for an accurate
prediction of turning points.
The Bayesian VAR provides empirical evidence on the dynamic interdependence between the
four macroeconomic variables, while the autoregressive process captures central dynamics of indi-
vidual time series. The example shows, however, that a certain degree of judgement is necessary to
adjust the forecasts. An advantage of these methods is that the forecasts are just based on a few past
observations and thus easy to understand and to adjust. The disadvantage of both methods is that no
causal interpretation can be given and that the forecasts do not give any information about underlying
structural sources of the projections.
5.2 Computing forecasts using structural models
Before investigating forecasts from a particular structural model, we first give a short overview of the
main steps taken in generating a model-based forecast. In doing so we closely follow the exposition
in Wieland and Wolters (2011). Structural macroeconomic models typically include unobservable
variables such as, for example, an output gap. A convenient tool for estimating such unobservables
is the Kalman filter. With regard to the forecasting process three aspects are best distinguished and
discussed separately: (i) model specification and solution; (ii) parameter estimation; (iii) and the
sequence of steps necessary to generate quarter-by-quarter forecasts. In the description of these steps
we will focus here on models estimated with Bayesian techniques.
Model specification and solution
A simple New-Keynesian model such as the one estimated by Del Negro and Schorfheide (2004)
serves as a good example. It is a log-linearized approximation of the original nonlinear model con-
sisting of three equations: a New-Keynesian IS equation that is derived from the household’s inter-
temporal first-order condition, a New-Keynesian Phillips curve that is implied by the price-setting
problem of the firm under monopolistic competition and price rigidity, and the central bank’s interest
rate rule:
xt = Etxt+1 − τ −1 (Rt − Etπt+1) + (1 − ρg)gt + ρzτ −1 zt (16)
πt = βEtπt+1 + κ(xt − gt) (17)
Rt = ρRRt−1 + (1 − ρR)(ψ1πt + ψ2xt) + ǫR,t (18)
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