Applications of Logit and Fuzzy Regressions for the Prediction of Economic Recessions in US Economy

In this paper we examine three binary regressions in order to predict the financial crisis or no crisis periods in USA. The first one is the Logit model and the other two are binary fuzzy regressions with sigmoid and triangular membership functions. We apply the models in period 1926-2005 and we examine the forecasting performance for the in-sample period as also in the out-of sample period, which is defined the period 2006-2009. Next we repeat the estimation process for the period 1926-2009 and we apply and examine the prediction and correctly classification percentage for the period 2010. We find that the forecasting performance of fuzzy triangle regression outperforms Logit model in the in-sample period 1926-2005, where the overall correct classification percentage of Logit regression is 80.74 per cent, while the overall forecasting performance of fuzzy regressions with sigmoid and triangular membership functions is respectively 77.19 and 91.09 respectively.Furthermore the fuzzy regressions outperforms significant the forecasting validity of Logit model, as with the last model we find an overall prediction percentage only of 62.50% in the out-of sample period 2006-2009, while with fuzzy regression we get 100.00 per cent forecast percentage. The empirical results indicate that fuzzy regressions provide a better and more reliable signal on whether or not a financial crisis will take place and are able to capture nonlinearities and imprecision than traditional econometric models do. Furthermore, based on the estimated values for the 2010 for the US economy factor we examine, we predict with all models that the economic recession will be continued through 2010