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Semiparametric logit model and linear logit model are both used in predicting bankruptcy in simple random and case-control data. Based on the findings of the study, SLM is recommended for prospective and retrospective types of data since it is more flexible and robust. An in-depth probe on the methodologies and their application, simulation studies and theoretical results are discussed.

A study examines the reasons for people to take part in genetic testing programs. Multinomial logit is used for the study.

The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of group differences in measurable characteristics, such as education, experience, marital status, and geographical differences to racial and gender gaps in outcomes. The technique cannot be used directly, however, if the outcome is binary and the coefficients are from a logit or probit model. I describe a relatively simple method of performing a decomposition that uses estimates from a logit or probit model. Expanding on the original application of the technique in Fairlie [3], 1 provide a more thorough discussion of how to apply the technique, an analysis of the sensitivity of the decomposition estimates to different parameters, and the calculation of standard errors. I also compare the estimates to Blinder-Oaxaca decomposition estimates and discuss an example of when the Blinder-Oaxaca technique may be problematic.

This paper suggests a method of decomposing differences in inter-group probabilities from a logit model and shows how it can be related to similar decompositions derived from a Oaxaca-Blinder framework. In so doing, it offers a solution to a problem, embedded within the Oaxaca-Blinder decomposition, relating to the appropriate choice of common coefficient vectors with which to evaluate the different attribute vectors. The decomposition method also shows how pair-wise comparisons of groups might be conducted in the presence of more than two groups, without discarding the information on groups excluded from the comparison. The proposed method is applied to inter-group differences in schooling participation in India and the results are compared with the Oaxaca-Blinder method. The decomposition is applied specifically to inter-group differences in the enrolment of boys at school in India. Keywords: Logit regression, school enrolment, India, decomposition methods

Experimental results on the viability of antitrust logit model to examine anticompetitive issues in horizontal merges in differentiated-product markets are presented.

A study estimates the value of arsenic-free drinking water for about 2700 houses in rural Bangladesh by applying multinomial logit model. The study revealed that the people were willing to pay 10 - 14 percent of the amount for piped water and 0.2 - 0.3 percent of the average household income.

Estimation of a freight transport demand function using a Conditional Logit model has emphasized the role of cost, transit time, and frequency of shipments as determinants of mode choice. Interviews with 157 forwarding forwarders, exporting firms and other firms located in the Valencian Community produced data on the mode of transport, price, transit time and other variables. Elasticity analysis revealed that sea transport was more sensitive to variations in its own costs and changes in transport prices as compared to road transport.

The mixed logit model provides a framework for studying discrete choice that avoids the Independence of Irrelevant Alternatives, permits coefficients to vary over a population and allows for incorporation of unobserved heterogeneity preferences.

The forecasting performance of a number of static random utility models of Lake Michigan salmon anglers is compared. The results reveal that both the random parameters logit (RPL) and latent class logit (LCL) models forecast equally well, by at least one measure the logit model outperforms them both, and also raise the possibility that recreational fishing decisions have a dynamic component.

Byline: Frederick L. Smyth (1), John J. McArdle (2) Keywords: affirmative action; college selectivity; engineering; gender; mathematics; minorities; SAT; science Abstract: Using Bowen and Bok's data from 23 selective colleges, we fit multilevel logit models to test two hypotheses with implications for affirmative action and group differences in attainment of science, math, or engineering (SME) degrees. Hypothesis 1, that differences in precollege academic preparation will explain later SME graduation disparities, was fully supported with respect to the outcome gap between Whites and underrepresented minorities, partially supported for that between Asians and underrepresented minorities, and between men and women. Hypothesis 2, that college selectivity, after accounting for student characteristics, will be positively associated with SME persistence, was not supported. We demonstrate that the significance of the selectivity effect is overestimated when unilevel models are used. Admission officials are advised to carefully consider the relative academic preparedness of science-interested students, and such students choosing among colleges are advised to compare their academic qualifications to those of successful science students at each institution. Author Affiliation: (1) Department of Psychology, University of Virginia, 102 Gilmer Hall, PO Box 400400, Charlottesville, VA, 22904-4400 (2) University of Virginia, USA Article History: Registration Date: 01/10/2004

Byline: Eleonora Bartoloni (1), Maurizio Baussola (2) Keywords: Innovation; logit models; technological diffusion Abstract: The adoption of new technologies in Italian manufacturing industries is analysed using data for 13,334 firms selected from the 1990--92 Community Innovation Survey. The determinants of technology adoption are analysed in an econometric framework (logit model) which is a general test of different theoretical explanations of technological diffusion. We particularly refer to the rank, epidemic and information effects which significantly affect the use of new technology in Italian manufacturing industries. We use a set of explanatory variables which enables us to set up a well specified empirical model and to use odds ratios to determine the effect of their changes on the adoption probability, thus giving a more precise picture of the determinants of technology adoption. Author Affiliation: (1) National Institute of Statistics (ISTAT), Milano, Italy (2) Department of Economic and Social Sciences, Catholic University, Piacenza Article History: Registration Date: 03/10/2004

A logit function of the distance can estimate the functional relationship between the random utility model and the logit model in competitive facility location, a proof that a relationship between the functional relationship exists. It also provides a theoretical proof for the use of the logit function of the distance in evaluating market share captured by competing facilities. The random utility model based on spatial choice behavior models establishes a connection between spatial choice models and the logit function of the distance approach.

Theory: This paper uses population ecology theory to account for variance in the dominance of institutions within lobbying communities, dominance first noted by Salisbury (1984). Hypothesis: We extract seven explanations from the literature on interest representation and organization ecology to account for variation in the dominance of institutions within interest communities. Method: OLS regression and ordered logit are employed to test the explanations using two sets of data on interest organizations in the American states: aggregate data on all 50 states and survey data on institutions registered to lobby in six states. Results: Empirical support is found for five of the seven explanations. We discuss the implications of these explanations for understanding how interest communities are constructed.

Byline: Anand N. Asthana (1) Keywords: demand analysis; water supply; developing countries Abstract: This paper examines how a policy of providing free domestic water can be unsustainable. Using a conditional logit model, this research looks at the demand side and identifies the determinants of choice. Under assumptions normally associated with developing societies, the model shows that the people's perception of benefits of safe water is significant, household variables like female literacy are important variables determining the choice of safe water and the capability to pay and willingness to pay already exists. The governments in poor countries need to have a fresh look at their policy relating to rural water supply. Author Affiliation: (1) Indian Administrative Service, D 4/13 Char Imli, Bhopal, 462016, India Article History: Registration Date: 18/10/2004

Byline: Albert A. Okunade (1), Robert L. Berl (2) Abstract: This study investigated the propensity of Business School alumni to donate cash to the alma mater. A logit regression model was fitted to the survey response data from the 1955/56--1990/91 alumni of a large, Carnegie-classified, Doctoral University I public higher educational institution. The marginal probability of giving was found to be significantly related to factors such as time since graduation, major area of degree, willingness to recommend the university to others, household attributes and family ties to the alma mater, the number of other voluntary donors known, and the availability of matching gift accounts where alumni are employed. The logit estimates can be used to predict the likelihood of giving and for selecting fund-raising prospects more efficiently. Potentially rich future research directions are explored. Author Affiliation: (1) Economics Department, The University of Memphis, Rm. 400BB (FCBE), Memphis, TN, 38152 (2) Department of Marketing, The University of Memphis, Rm. 302BB (FCBE), Memphis, TN, 38152 Article History: Registration Date: 22/10/2004

The valuation of a set of assets in its net present value is presented using multinomial logit models in a decision analysis framework. The analysis framework provides expected monetary value rather than estimators of the firm's financial state. This method can be used in analyzing simultaneous projects in one organization demonstrating that multinomial logit models are useful in improving asset valuation.

Wildlife managers should use the qualitative dependent variable technique to make models for understanding the likelihood of a certain constituency engaging in wildlife-related recreational activities. Mathematical or logit models show, for instance, that wildlife excursions and trips are generally conducted away from home and so, planning bodies should promote sites for wildlife recreation. Knowledge of the public response toward wildlife can help formulate plans to save ecological wealth.

The effects of sociodemographic and political variables on causal attribution for poverty were measured among college students using the LOGIT form of the general logistic regression. Consistent with past research, our study indicated that students reporting higher parental social class, conservative political orientations, and opinions in favor of the death penalty were significantly more likely to agree with an individual causal attribution for poverty than other students when the effects of all other variables were held constant. It is suggested that such variables be examined as developmental factors in differences in attributional styles., A study of university students' causal attribution for poverty were examined through sociodemographic and political correlates. The study suggests that students reporting higher parental social status, conservative political orientation and opinions in favor of death penalty are more likely to agree with individual casual attribution for poverty, when all other variables are kept at constant. The attribution for poverty among college students are measured using the LOGIT form of the general logistic regressions which suggest that variables must be examined as developmental factors in variations in attributional styles.

The conditional logit model is a multinomial logit model that permits the inclusion of choice-specific attributes. This article shows that the conditional logit model will maximize entropy given a set of attribute-value preserving constraints. A correspondence between the maximum entropy (ME) and maximum likelihood (ML) estimates for logit probabilities is established. Some easily computable and useful diagnostics for logit analysis are provided, and it is shown that an evaluation of the relative importance of attributes can be made using the ME formulation. The ME formulation is also generalized to accommodate initial choice probabilities into the logit model. An example is given. KEY WORDS: Choice models; Entropy; Kullback-Leibler discrimination information function; Relative importance., 1. INTRODUCTION The multinomial logit model is widely used in management and social research problems. It models the probability that an individual i selects choice [C.sub.ij] from a set of [...]

An artificial neural network used in predicting the probability of failure for savings and loan associations is evaluated against a logit model. Results show that the network is more reliable than the logit model in predicting failure outcomes for thrift institutions. This implies that neural networks may serve as models for failure prediction although its inability to provide adequate explanations for the institution's failure may be restrictive.

Ordered categorical dependent variables were analyzed using linear regression and multinomial logit methods. The results show that an ordered-response logit model is the most appropriate method for evaluating theoretical problems, parsimony and non-tested hypotheses. It allows the researcher to relax rigid assumptions regarding interval levels of measurement. On the other hand, linear regression analysis of ordinal dependent variables is not recommended since this method leads to biased results.

Candidate variables in choice-based conjoint analysis were screened by calculating their weighted correlations against residuals from the last fitting multinomial logit model (MNL). Passing candidate variables were then subjected to diagnostic tests prior to each iteration cycle of the refitted MNL. Application of this stepwise procedure on a larger set of data is illustrated.

Unnecessarily awkward procedures for interpreting logit coefficients in terms of odds-ratios or ratios of odds-ratios appear in sociological research. Because odds-ratios are not linearly related to probabilities, statements about relative odds-ratios do not identify the effects of group membership as measured by dummy independent variables on a dichotomous dependent variable. Procedures from econometrics allow calculating the probabilities of being in the category of interest of a dichotomous dependent variable. The effects of dummy independent variables equal the differences in probabilities among the different groups. For continuous independent variables, the maximum possible effect on changes in probability is always one-quarter of the value of the logit coefficient.

The use of logit and probit models have been growing since the 1980's as part of management research. A lack of good understanding on how these models differ from ordinary least squares could lead to misinterpretation of data.

A study on willingness-to-pay distributions of transportation industry, using mixed logit model, is presented.

A multinomial logit model, comprising of missing information or the 'don't know' responses, for contingent valuation (CV) analysis is discussed.

This paper extends the conditional logit approach (Rasch, Andersen, Chamberlain) used in panel data models of binary variables with correlated fixed effects and strictly exogenous regressors. In a two-period two-state model, necessary and sufficient conditions on the joint distribution function of the individual-and-period specific shocks are given such that the sum of individual binary variables across time is a sufficient statistic for the individual effect. By extending a result of Chamberlain, it is shown that root-n consistent regular estimators can be constructed in panel binary models if and only if the property of sufficiency holds. In applied work, the estimation method amounts to quasi-differencing the binary variables as if they were continuous variables and transforming a panel data model into a cross-section model. Semiparametric approaches can then be readily applied.

Given sound theoretical underpinnings, the random utility maximizationbased conditional logit model (CLM) serves as the principal method for applied research on industrial location decisions. Studies that implement this methodology, however, confront several problems, notably the disadvantages of the underlying Independence of Irrelevant Alternatives (IIA) assumption. This paper shows that by taking advantage of an equivalent relation between the CLM and Poisson regression likelihood functions one can more effectively control for the potential IIA violation in complex choice scenarios where the decision maker confronts a large number of narrowly defined spatial alternatives. As demonstrated here our approach to the IIA problem is compliant with the random utility (profit) maximization framework.