Fabio Canova 91To conclude, we present two examples below illustrating the issues we have
discussed in this section. In the first example, noninvertibility emerges because
the model has a nonfundamental representation. In the second, the MA of the
model is invertible, but the dynamics of the reduced system are different from
those of the full one.
2.3.1.1 Example 3: a Blanchard and Quah economy
The example we present belongs to the class of partial equilibrium models popular
in the late 1980s. While it is not difficult to build general equilibrium models
with the required features, the stark nature of this model clearly highlights how
invertibility problems could occur in practice. The model that Blanchard and Quah
(1989) consider has implications for four variables (gross domestic product (GDP),
inflation, hours and real wages) but the solution is typically collapsed into two
equations, one for GDP growth (GDP), the other for the unemployment rate
(UNt), of the form:
GDPt= (^3) t− (^3) t− 1 +a(
1 t− (^1) t− 1 )+ (^1) t (2.18)
UNt=− (^3) t−a (^1) t, (2.19)
where (^1) tis a supply shocks, (^3) ta money supply shock andais a parameter mea-
suring the impact of supply shocks on aggregate demand. Hence, the aggregate
decision rule for these two variables is a VMA(1). It is easy to check that a finite-order
VAR may approximate the theoretical dynamics of this model only ifa>1.
To see this, we seta=0.1 and plot in Figure 2.5 the theoretical responses of
output and unemployment to the two shocks and the responses obtained using a
VAR(1) and a VAR(4), where the econometrician uses the correct (but truncated)
vector autoregressive representation of the model. Note that, while the signs of
the responses are correct, the dynamics are very different. Also, while there is
some improvement in moving from a VAR(1) to a VAR(4), some of the theoretical
responses are very poorly approximated even with a VAR(4). Since a VAR(q),q>4,
has responses which are indistinguishable from those of a VAR(4) – as the matrices
on longer VAR lags are all very close to zero – no finite-order VAR can capture (2.17)
and (2.18).
What generates this result? Whena<1 the aggregate decision rules of the model
are nonfundamental, that is, innovations to output growth and unemployment do
not have the same information as the variables themselves. Therefore, there is no
convergent VAR representation for these two variables where the roots of the VAR
are all less than one in absolute value, and this is true even when an infinite lag
length is allowed for.
2.3.1.2 Example 4: an RBC model
We work with the simplest version of the model since more complicated structures
do not bring additional insights into the problem. The social planner maximizes:
E 0
∑∞
t= 0
βt
c^1 t−φ
1 −φ
−ANt, (2.20)