16 Pooling monthly data for five countries since 1985, we estimated a Logit model<sup>37</sup> (Table 7) with satisfactory results.<sup>3 8</sup> All of the domestic variables were significant and had the expected sign. Volatility of domestic credit and volatility of asset returns affect positively the probability of bubble occurrence, as predicted by the Allen-Gale model. So do termsof-trade (positive) shocks and capital flows. Domestic credit growth has a negative sign 12 months prior to the bubble, but the sign is positive 18 months prior to the bubble, reflecting the non-linear effect of this variable.

Regarding the external variables, a striking result was the opposite sign of the overvaluation proxies for stock markets and real estate. When stock markets in the U.S. are overvalued, the probability of a bubble occurrence in Latin American stock markets declines. However, the probability is positively associated with overvaluation in real estate markets. The only way to reconcile this result is through the negative or low positive correlation between these asset classes in the U.S. The term structure spread is significant and has the expected sign: higher-term structure spreads are leading indicators of expanding economic activity or lower future real interest rates (Estrella and Mishkin, 1997 and Fama, 1990) The marginal probabilities (Table 8) that tell us how the probability of a bubble changes with a unit change in each individual variable were calculated by estimating the marginal effect at each observation and then taking the sample average. The differences across countries arise because of the fixed-effects coefficient, and because the values of the explanatory variables are different for each country. The term spread in the U.S. has the largest marginal impact on bubble probability. This statistical result is confirmed by observing the correlation between the term spread and the residuals of the intrinsic bubble model (Graph 19), which is surprisingly close in the Mexican case. The second most influential variable is domestic credit growth; the volatility of real asset returns and terms-of-trade shocks are of similar importance.

Even though our results are not directly comparable to Harvey's (1995) or to Hargis and Maloney's (1997), given that they examine the impact of a set of variables on returns and we examine the impact of a set of variables on the probability of bubble occurrence, the general outcome reflecting the extent of the influence of global factors seems quite different. Part of the discrepancy can be explained by the sample period, since both papers examine data from 1976 to 1993, missing a great part of the economic developments of the 1990s which seem to dominate our results.

37 We estimate both Logit and Probit models, and the results are almost identical (see Appendix D). However, we present the Logit exercise in the main text of this section, given the possibility that in some cases Probit models do not produce consistent estimators with fixed-effects panel methods (Hsiao, 1997 and Green, 1999).

38 The model was estimated with instrumental variables to control for potential endogeneity of the domestic variables. Additionally, to avoid difficulties in the interpretation of the marginal probabilities, as well as to facilitate comparison across variables, we employed standardized variables (subtracting the mean and dividing by the standard deviation of the series) to circumven **t** he units problem. This does not alter the signs or significance of the results obtained with the raw data.