...occurrence financial crises. An EWS usually involves the use of a consistent framework to analyze high-frequency macro-prudential indicators.

Experience so far suggests that there are limitations to the predictive power of most EWSs. For instance, although the two International Monetary Fund (IMF) core EWSs (the Developing Country Studies Division Model and the Modified Kaminsky, Lizondo and Reinhart Model) correctly predicted that a crisis was impending in Turkey one year before it actually broke out in February 2001, the models did not issue any warning signals for the January 2002 crisis in Argentina (Anon, 2002). It is generally acknowledged that it is not easy to predict the occurrence of future financial crises, given that no two crises are the same in terms of the factors that cause them, the increasing volatility of financial markets, and the scale of contagion. Furthermore, as markets become more globalized, it is becoming more difficult to isolate the impact of external events within a domestic economy.

In view of the difficulty of predicting a crisis, this paper develops a model that can assess the level of resilience of an economy as a supplement to an EWS. The major difference between an EWS and a resilience framework is that a resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand financial shocks. The concept of an EWS is based on the premise that an economy and its financial markets will behave differently before an imminent financial (banking, currency, or debt) crisis, and that such "abnormal" behavior has a systemic and recurrent pattern that is discernible. Therefore, one may judge whether a crisis is coming from the movement of particular economic and financial indicators.

The concept of resilience has received considerable attention in economic- ecological modeling in the last decade (see, for example, Batabyal (1998) and Levin et al. (1998)), although the concept has not been widely applied to financial market studies. The concept of resilience is based on the hypothesis that different states of a system involve different equilibria. It is believed that if an economic system is resilient, then it should be capable of addressing new challenges and sudden qualitative shifts. In other words, if an economy has less resilience, then the chance that it will change from its current state to other states is greater. In the context of economic and financial systems, resilience can be interpreted as a measure of the ability of a system to remain stable. It should be noted that the measurement of resilience does not involve the anticipation of financial shocks.

Although the concept of resilience is plausible, the question of how to quantify a measure of resilience remains open. One major difficulty is that there are no suitable response variables with which to measure resilience. It is possible to properly define a financial crisis and use it as the response variable in the context of an EWS, which means that an EWS can be regarded as a supervised learning system, but for a resilience measure we need to come up with a model in an unsupervised learning situation. Further details about supervised and unsupervised learning can be found in Breiman (2001) and Hastie et al. (2001).

In this paper, a statistical learning approach that incorporates expert opinion is proposed to measure the resilience of an economy. As expert opinions are usually given in terms of approximate reasoning, a fuzzy logic approach (Tanaka, 1997; Fuller, 2000) is employed to model and capture the ambiguous logical reasoning of expert opinions to assign tentative resilience scores to an economy, which comprises the first stage of the approach. In the second stage, a classification tree (Breiman et al., 1984) is built to extract thresholds for the economic indicators. The output from the tree is fed back to experts to check for consistency. Thus, a feedback learning system is developed and a final resilience scoring scheme is derived in the form of a decision tree that has very little (if any) inconsistency with expert opinion.

One main advantage of the classification tree is that a decision rule can be visualized from which experts can deduce economic interpretations. The future resilience scores of an economy can also be derived by examining the relevant indicators according to the classification of the tree.

The remaining part of this paper is organized as follows. Section 2 introduces the basic framework for the resilience indicators, and the framework is applied to study a large sample of economies. The empirical findings are reported in Section 3, and a conclusion is given in Section 4.

2. Basic framework

The basic conceptual framework for the resilience indicators involves the assessment of resilience in five sectors: (i) the external sector; (ii) the public sector; (iii) the banking sector; (iv) the corporate sector; and (v) the household sector. These five sectors are all considered in an EWS. In each of these sectors, three to five key indicators are selected to reflect the strength and weakness of the sector. Most of the indicators are developed from the financial soundness...

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