We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...

The Nested Multinomial Logit (NMNL) model is used extensively in modeling consumer choices among discrete alternatives when the number of alternatives is large. Unfortunately, applied researchers often find that estimated NMNL models fail to meet the Daly-ZacharyMcFadden (DZM) sufficient conditions for consistency with stochastic utility maximization. Borsch-Supan (1990) provides a relaxed set ...

The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive computational mistakes. In this article, I review a menu options interpret results logistic regressions correctly effectively using Stata. consider marginal effects, partial (contrasts of) predictive margins, elasticities, odds risk ratios. also show that interaction term...

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and probit models to consider the interaction among the attributes. Besides the DM's attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make us...

It is well understood that the two most popular empirical models of location choice conditional logit and Poisson return identical coefficient estimates when the regressors are not individual specific. We show that these two models differ starkly in terms of their implied predictions. The conditional logit model represents a zero-sum world, in which one region's gain is the other regions' loss....

The main feature of the CoIL challenge data is that the observed response is discrete: we only observe whether or not the customers buy caravan insurance. Models for such data were developed in the 1940s and 1950s in the Bio-statistics literature (see references in Cox and Snell, 1989), in particular LOGIT and PROBIT. In the 1970s such techniques became popular in Economics, where a utility max...

Despite the vital role of utility functional form in welfare measurement, the implications of working with incorrect utility specifications have not been examined in the choice experiments (CE) literature. This paper addresses the importance of the specification of both non-monetary attributes and the marginal utility of income. Monte Carlo experiments have been conducted wherein different attr...

Predicting financial distress is one of the most well-known issues in corporate finance. Investors and other stakeholders often use prediction models as relevant tools for identifying weaknesses to eliminate potential threats business partners. This paper aims present an effective logistic regression model a one-year-ahead with minimum set predictors part risk management. The motivated by vario...