Exchange Rate Economics: Theories and Evidence

Empirical evidence on the monetary approach 153
generating process,should do better than a static model or one with very simple
dynamics (which is essentially the kind of model used by other researchers).
MacDonald and Marsh focus on the yen,mark and pound against the US dollar,
over the period January 1974–December 1992,with the last 24 observations held
back for forecasting purposes. The forecasts constructed are fully simultaneous
and dynamic and could therefore have been used by a potential forecaster. The
success of the forecasts is gauged in three ways. First,using the RMSE r criterion,
discussed earlier. Second,in terms of the directional ability calculated as:
D =
(1 if forecast direction = actual,else 0)
. (6.33)
n
On the basis of chance D is expected to be 0.5,and therefore any number above
0.5 means that the model does better in terms of its predictive ability than simply
tossing a coin. Finally,RMSE ratios were constructed for the model projections
relative to a panel of 150 professional forecasts,located in the G7 financial centres,
and as collected by Consensus Economics of London (gauged using the RMSE r
criterion).
In Table 6.5 we present a representative SEM model for the UK–USD. This
table should be read in the following way. The dependent variable for each
equation is noted in the first column (so,for example,the first equation is for
the change in the exchange rate). The cells in the columns under A 0 contain contemporaneous
(i.e. period t) interactions amongst the variables,while the cells in
the A i block contain the coefficients on the lagged variables. Finally,the coefficients
on the ECM terms (i.e. the α coefficients) are contained in the final two
columns. The estimated system reported in Table 6.5 reveals that there are a
number of significant simultaneous interactions,complex dynamic relationships
that drive the dependent variables and also the importance of the error correction
terms which are numerous and also highly significant in all cases. The forecasting
results are summarised in Table 6.6. We note that for the pound sterling the
model beats the random walk at 2 months ahead,while for the yen and mark
forecastability kicks in at 3 and 4 months ahead,respectively. Note that many of
the RMSE ratios are significantly less than unity and this is especially so for the
pound sterling. All of the models seem to have very good directional forecasting
powers and across the three currencies and forecast horizons,our models outperform
the professional forecasters. It is also worth noting that the RMSE ratio of
our SEM model relative to a VAR in first differences is always less than unity.
This would seem to underscore the point that such models are likely to underperform,both
because they are over-parameterised and also because of their
failure to incorporate the ‘long-run’ information contained in the cointegrating
vector.
In a follow-up paper,MacDonald and Marsh (1999) extend their earlier analysis
by using a tripolar model of the yen,dollar and German mark (the long-run
relationships for this model are discussed in Chapter 9). An example of one of the