4 This aggregation method differs from Kaminsky's since the signals are extracted from the behavior of our composite index, while in her case each individual variable generates signals that are then aggregated into the composite index. Our aggregation procedure assumes that the leading variables drift more or less in the same direction, or have a common element in their behavior prior to the crisis. If this is not the case, it will not be a good indicator. For example, if the real exchange rate appreciates (increases) but there is a contraction in domestic credit growth, our IMV may not change and hence no signal will be issued.

Once the composite index of macroeconomic vulnerability (IMV) is built, its sinmple evolution is not informative enough about a potential risk situation. Thresholds have., to be used in assessing when the IMV has reached an "anomalous" level.

We applied four transformations or filters to the IMV to generate signals: detrending the variable with respect to its long-run level (defined as the Hodrick-Prescott trend), the variable in levels (no iFilter), detrending the variable with respect to a short-run (6 mos.) moving average and thLe residuals of an ARIMA model fitted to the IMV. The first three (or variants) have been used in other papers while the ARIMA residuals approach is novel, and produced the lowest Type I error (probability of not anticipating the crisis). Initially we focus on the IMV as described above, to make our sample period as comparable as possible to that of other papers. In a second stage stock market prices in real terms are included as an additional leading variable, but the sample period is shortened to 1986-199,8 due to availability of consistent information from the IFC on this variable for most of the sample countries.

# A. Deviations from trend model (DT model)

This model uses deviations from a long run trend to generate signals. To determine wriat the long run trend of a series is, the IMF used a 3-year moving average, but we prefer-ed the Hodrick-Prescott filter since the first procedure induces autocorrelation to he detrended series. This is an undesirable feature since once the mechanism sends a sigi a;, it will tend to produce signals in successive periods. On the other hand, the HodrilkPrescott filter induces spurious cycle behavior when applied to non-stationary data so _we_ have to be aware of this phenomenon induced by the detrending procedure.

The deviations from the trend for each of the variables were standardized and aggregaled to build the Index of M/Iacro Vulnerability (IMV). The index was computed alternatively, including inflation or not, and based on simple Granger causality tests between thl, se indexes and the crises with lags up to 24months, the best index was chosen. Inflation was an informative variable only in the cases of Argentina and Mexico. This IMV signal i a crisis when it surpasses a threshold determined by the mean plus 1.5 standard deviatiors.

8 Cogley, T. and J. Nason (1995) "Effects of the Hodrick-Prescott filter on trend and difference stationary time series: Implications for business cycle research", Journal of Economic Dynamics and Control, 19, pp.253-2<sup>7</sup> 8.