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Monetary and Exchange Affairs Department (MAE), IMF, and Faculty of Economics and Politics, University of Cambridge, respectively. We would like to thank Sandra Marcelino for superlative research assistance, and participants at a seminar at MAE for helpful comments and suggestions.
The joint World Bank-IMF Financial Sector Assessment Program (FSAP) was introduced in May 1999 to assess financial system soundness in member countries.
A good explanation of the GTSA is as follows: “(1) Formulate a general model that is consistent with what economic theory postulates are the variables entering any equilibrium relationship and which restricts the dynamics of the process as little as possible; (2) Re-parameterize the model to obtain explanatory variables that are near orthogonal and which are ‘interpretable’ in terms of the final equilibrium; (3) Simplify the model to the smallest version that is compatible with the data (‘congruent’); (4) Evaluate the resulting model by extensive analysis of residuals and predictive performance, aiming to find the weaknesses of the model designed in the third step.” (Pagan, 1995). See Davidson and Hendry (1981) for a discussion of the limitations of GTSA, and for recent applications of GTSA see, for example, Campos and Ericsson (2000) and White (1999).
See Davidson and Hendry (1981) for a discussion of the limitations of GTSA, and for recent applications of GTSA see, for example, Krolzig and Hendry (2000), Campos and Ericsson (2000) and White (1999).
The approach used here is similar in spirit to recent revionist approaches to modeling economic growth, which poses several of the same econometric challenges as does analysis of financial crises. Brock and Durlauf (2000) explicitly analyze theory uncertainty and parameter uncertainty using a decision-theoretic framework. Fernandez, Ley and Steel (2001) address the robustness problems posed by the coincidence of a large number of potential explanatory variables and the relative weakness of economic theory by using a full Bayesian approach.
Modelling of the likelihood of crisis the procedure is exactly the same, with the only difference being that maximum likelihood is used to estimate binary probit models.
Brock and Durlauf (2000) discuss the use of qualitative information in establishing identifying assumptions in empirical analysis of growth models.
This criteria is distinct from a number of alternate DTA objective functions, such as the Bayesian Information Criterion (BIC) derived by Schwarz (1978) which Burnham and Anderson (1998) note, are “dimension consistent.” The BIC is used to locate the “true” model, which is fixed as sample size increases, and assumed to lie within the candidate set of models. Consistency means that as sample size increases, the probability of locating the true model approaches one. In the context of empirical analyses of financial crises, an increase of sample size corresponds either to a larger set of countries, e.g., extending the dataset from the standard 30 or so large emerging market countries to include smaller emerging market countries, or to a large set of indicators, e.g., extending the dataset to include corporate or legal indicators. The problem is that both extensions may well introduce an alternative data generating process, thereby violating consistency.
The pseudo r-squared equals 1 when the model is a perfect predictor of crisis events; type 1 errors represent the percentage of crisis observations that we were predicted as non-crisis; type 2 errors represent the percentage of non-crisis observations that were predicted to be crises (false alarms).
Sundararajan and Baliño (1991) and Goldstein and Turner (1996) provide overviews of bank crises. More recent bank crises are not characterized by extensive runs on deposits (Demirgüç-Kunt et al., 2000). Government guarantees are stressed by by Dooley (1997) and Krugman (1999a) but these seem to have receded as a focus of the crisis literature.