Exchange Rate Economics: Theories and Evidence

344 Market microstructure approach
be gauged by incorporating a piece of the common information set – say past
exchange rate changes or the forward premium,– into equation (14.6),that is:
s e j,t = X ′
t + g ′
j + β j s t−1 + u j,t ,(14.7)
where X t ′ is the common forecast based on I less the lagged exchange rate change
and g
j ′ is the new individual bias. Specifying the equivalent equation for the average
forecast and subtracting as earlier implies that:
s e jt − se At = g ′
j +[β j − β A ]s t−1 +[u jt − u At ]. (14.8)
Individual biases exist if in an estimated version of (14.8) g ′
j
̸= 0,while
idiosynchratic effects exist if β j − β A ̸= 0.
In implementing (14.8),Ito (1990) uses the data set described in Section 14.1.1
and finds that there are important differences between the different industrial
classifications. In particular,he finds that around half of the forecasters have significant
individual effects. This breaks down in the following way. First,exporters
and trading companies are significantly heterogeneous for each of the three horizons
and this is revealed solely in terms of individual biases (idiosynchratic effects
are not significant). Constant biases are also shown to be important for importers
at the 1-month horizon and banks at the 6-month horizon. Ito also reported
‘group effects’ which relate to the fact that forecasters in the export sector are
biased towards yen depreciation (relative to others) and importers are biased
towards a yen appreciation (relative to others),and this is described as ‘wishful
expectations’.
Due to the international nature of their database,MacDonald and Marsh
(1996b) are able to push the Ito tests a little further. In particular,the Consensus
data set allows calculations of the earlier effects with respect to both the overall
average and also to the relevant country average. This,in turn,allows an examination,amongst
other aspects,of whether individuals are more inclined towards,
say,a dollar appreciation than the overall average or their fellow countrymen. The
distinction may be important if the information set is not common to all forecasters,
due to time zone or language-induced informational differences: averaging within
a country is less likely to result in problems arising from such informational asymmetries.
MacDonald and Marsh find very strong evidence of heterogeneity for both
the 3- and 12-month horizons. Using the country average or the ‘world’ average
does not change this result,and MacDonald and Marsh infer from this that the
informational asymmetries between nations are small and insignificant. In contrast
to Ito,MacDonald and Marsh also find evidence of idiosynchratic effects using
both the forward premium and the exchange rate change.
Having established that forecasters are heterogeneous,MacDonald and Marsh
then go on to test whether such differences of opinion translate into different forecast
performances using a version of Prentice’s reduced rank statistic. Overall they
find that differential forecast performance is indicated for all three currencies at
both forecast horizons,but is strongest for the 12-month forecasts and especially