Exchange Rate Economics: Theories and Evidence

150 Empirical evidence on the monetary approach
respectively. For all three panels the null hypothesis of no cointegration between
the exchange rate and monetary fundamentals is rejected at the 5% level. Mark
and Sul then go on to use these panels to construct out-of-sample forecasts and
this part of their exercise is discussed in more detail in the next section.
6.3.2 Out-of-sample forecasting revisited
The Meese and Rogoff (1983) finding has had an enduring impression on the
economics profession. For example,surveying the post-Meese and Rogoff literature
Frankel and Rose (1995a) argue (emphasis added): ‘the Meese and Rogoff
analysis of short horizons [less than 36 months] has never been convincingly
overturned or explained. It continues to exert a pessimistic effect on the field
of empirical exchange rate modeling in particular and international finance in
general.’
One potential reason why Meese and Rogoff may have been unable to beat
a random walk is because all but one of their empirical relationships were either
static or had very limited dynamics. However,we know from our discussions of
the PPP proposition,which as we have seen underpins the monetary model,that
exchange rate dynamics tend to be quite complex and adjustment to PPP takes
a considerable number of periods. A similar story is true for the money market
relationships which are so central to the monetary model – all of the available
evidence from money demand studies indicates that adjustment to equilibrium is
often quite complex. Clearly,for an empirical exchange rate model to be successful
it should incorporate these kind of dynamics. As we shall now demonstrate,when
these dynamics are accounted for in the estimation process the random walk model
is convincingly beaten.
One potential reason for the dynamics in the relationships underpinning the
monetary model is structural instability. One way of allowing for such instability
would be to let the coefficients in the reduced-form equation evolve over time and
this has been done in a number of studies,such as Wolff (1987) and Schinasi and
Swamy (1987). These studies report a consistent outperformance of the random
walk model at horizons as short as 1 or 2 months.
Another way of addressing the dynamic adjustments underlying (6.1),(6.7) and
(6.11) is to use a modelling method such as the so-called general-to-specific dynamic
modelling approach proposed by Hendry and Mizon (1993) and others. Although
in one of their estimated models,Meese and Rogoff did allow for rich dynamic
interactions using a VAR,it is likely that such a system is over-parameterised in
terms of its use of information and such systems generally do not forecast well.
Interestingly,Meese and Rogoff in a footnote cite this as a potential reason for the
poor performance of the VAR-based implementation of the model. The generalto-specific
approach can be used to produce parsimonious VARs or parsimonious
VECM models.
The general-to-specific approach to exchange rate modelling,and its implications
for exchange rate forecastability,can be illustrated using the approach of
MacDonald and Taylor (1991). In particular,they take the significant cointegrating