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There are a lot of techniques and methods for prediction of bankruptcy among them “Statistical methods” or econometrics techniques are more popular. As dependent variable in our study is qualitative it is convenient to use qualitative discrete models. Mixed Logit model is one of the powerful and flexible techniques of discrete choices that allow the coefficients to be random with distribution function. Explanatory variables are financial ratios which derived from Zmijewski’s model. The sample data are from Tehran Stock Exchange’s Brokerage Companies during 2001-2008. We selected two random samples, one for estimation and another for prediction power test. Results show that the degree of successfulness of the model is over 90 percent.

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Price shocks lead to oil price volatility in world oil markets. In response to this volatility, economic growth may take different regime and behavior patterns in different situation. Investigating this multi behavior patterns can be useful for policymakers to reduce the effect of oil price volatility. In this study, an EGARCH model has developed using the seasonal data of OPEC oil basket nominal prices during 1367:Q1-1389:Q4. Markov switching models is also applied to investigate the multi behavior patterns of economic growth in response to oil price volatility in Iran. The results show that positive oil price shocks sharply lead to formation of oil price volatility, but, the negative price shocks will slightly reduce oil price volatility. Iranian economic growth is affected by this volatility under three different behavior regimes. If the economy switch to one of the regimes (low, medium, high economic growth), the probability of transition between these regimes and their duration is different. So, oil price volatility as a reason for low economic growth in Iran may cause the economy switch to its lower situation.