IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T21:40:09ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2013-07-10T10:59:17Z2013-07-10T10:59:17Zhttp://eprints.imtlucca.it/id/eprint/1641This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/16412013-07-10T10:59:17ZStudy of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USAIn this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and
the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined
and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms
the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.Eleftherios Giovaniseleftherios.giovanis@imtlucca.it2013-07-09T14:50:01Z2013-07-09T14:50:01Zhttp://eprints.imtlucca.it/id/eprint/1637This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/16372013-07-09T14:50:01ZApplication of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economyPurpose – The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.
Design/methodology/approach – A logit regression was applied and the prediction performance in two out-of-sample periods, 2007-2009 and 2010 was examined. On the other hand, feed-forwards neural networks with Levenberg-Marquardt error backpropagation algorithm were applied and then neural networks self-organizing map (SOM) on the training outputs was estimated.
Findings – The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011-2012, if government does not apply direct drastic measures.
Originality/value – Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.Eleftherios Giovaniseleftherios.giovanis@imtlucca.it2013-07-08T13:33:12Z2014-01-24T14:25:00Zhttp://eprints.imtlucca.it/id/eprint/1627This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/16272013-07-08T13:33:12ZARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A.This paper examines the estimation and forecasting performance of ARIMA models in comparison with some of the most popular and common models of neural networks. Specifically we provide the estimation results of AR-GRNN (Generalized regression neural networks) and the AR-RBF (Radial basis function). We show that neural networks models outperform the ARIMA forecasting. We found that the best model in the case of real US GNP is the AR-GRNN and for US unemployment rate is the AR-MLP. Eleftherios Giovaniseleftherios.giovanis@imtlucca.it