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Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

Discrete choice modeling has been discussed by both academics and practitioners as a means of analytical support for B2C relationship marketing. This paper aims to discuss applying this analytical framework in B2B marketing, with an example of cross‐selling high‐tech services to a large business customer. This example is also used to show how an algorithm of genetic binary choice (GBC) modeling, developed by the author, performs in comparison with major techniques used nowadays, and to analyze the financial impact of these different approaches on profitability of B2B relationship marketing operations.

Predictive models based on the regression analysis, the classification tree and the GBC algorithm are built and analyzed in the context of their performance in optimizing cross‐selling campaigns. An example of business case analysis is used to estimate the financial implications of the different approaches.

B2B relationship marketing, although differing from B2C in many aspects, can also benefit from analytical support with discrete choice modeling. The financial impact of such support is significant, and can be further increased by improving the predictive accuracy of the models. In this context the GBC modeling algorithm proves to be an interesting alternative to the algorithms used nowadays.

The generalizability of the findings, concerning performance characteristics of the algorithms, is limited: which method is best depends, for example, on data distributions and the particular relationships being modeled.

The paper shows how B2B marketing managers can increase the profitability of relationship marketing using discrete choice modeling, and how implementing new algorithms like the GBC model presented here can allow for further improvement.

The paper bridges the gap between research on binary choice modeling and the practice of B2B relationship marketing. It presents a new possibility of analytical support for B2B marketing operations together with financial implications. It also includes a demonstration of an algorithm newly developed by the author.

The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover.

The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated.

When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study).

This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

Peers and friends are among the most influential social forces affecting adolescent behavior. In this chapter, the authors investigate peer effects on post high school career decisions and on school choice. The authors define peers as students who are in the same classes and social clubs and measure peer effects as spatial dependence among them. Utilizing recent developments in spatial econometrics, the authors formalize a spatial multinomial choice model in which individuals are spatially dependent in their preferences. The authors estimate the model via pseudo maximum likelihood using data from the Texas Higher Education Opportunity Project. The authors do find that individuals are positively correlated in their career and college preferences and examine how such dependencies impact decisions directly and indirectly as peer effects are allowed to reverberate through the social network in which students reside.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

This paper aims to identify the key factors in children's choice of clothing from six to 11‐year‐olds.

This exploratory study was developed through a questionnaire answered by 313 children, between the ages of six and 11, from four different schools in Porto (Portugal), covering the private/ state and the rural/urban dimensions. The Logit and Probit binary choice models have been chosen to evaluate the factors that influence children's choice of clothing (proxy), based on a mix constituted by “brand name, functionality and fashion”.

The results showed that choice is positively related to age, sex, environment, parents' income, self‐esteem, susceptibility to interpersonal influence and utilitarian value (functional value). On the contrary, susceptibility to reference group influence, materialism (materialistic attitudes), ostentatious value and involvement are negatively related to choice.

The results of the research are limited by the specific sample chosen for this purpose. It is very difficult to generalize the results taking into account all children. The translation and adaptation of the original scales to the case of children and to the Portuguese context may have caused some deviation. The determination of choice was not done directly, as it resulted from a proxy related to the choice of clothing.

Retailers and manufacturers of children's clothing can benefit from the findings of this study. The model developed was applied to the children's choice of clothing. However, it may be tested for a wider universe and applied to other product types.

This study contributes to the literature by studying key factors that affect children's choice of clothing. Based on these results, indications for future research are pointed out: a change in the mix of clothing products available and a characterization of choice types through free, conditional, ordered and multinomial models.

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross-sectional and panel data models, and in this chapter we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point-identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a “non-standard” exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.

We derive marginal conditions of optimality (i.e., Euler equations) for a general class of Dynamic Discrete Choice (DDC) structural models. These conditions can be used to estimate structural parameters in these models without having to solve for approximate value functions. This result extends to discrete choice models the GMM-Euler equation approach proposed by Hansen and Singleton (1982) for the estimation of dynamic continuous decision models. We first show that DDC models can be represented as models of continuous choice where the decision variable is a vector of choice probabilities. We then prove that the marginal conditions of optimality and the envelope conditions required to construct Euler equations are also satisfied in DDC models. The GMM estimation of these Euler equations avoids the curse of dimensionality associated to the computation of value functions and the explicit integration over the space of state variables. We present an empirical application and compare estimates using the GMM-Euler equations method with those from maximum likelihood and two-step methods.

A binary choice model explaining the distribution of holidays abroad undertaken by UK residents is constructed and estimated. The foreign holiday demand function is generated from a comparison of holiday costs and benefits, and stochastic behaviour is permitted. In addition, the effects of incomplete knowledge on holiday choice are incorporated in the model. It is shown that the empirical results support the theoretical framework and that the £50 foreign currency limit imposed by the British Government between 1966 and 1969 resulted in a shift in the distribution of foreign holidays.