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The purpose of this paper is to reveal the core determinants and adoption patterns of the major enterprise information systems.

This study incorporated the core representative and meaningful explanatory variables in the major previous literatures and analyzes the core determinants of businesses' adoption of the essential information systems and the substitutionary patterns among them, using a Bayesian multivariate probit model, which is based on McFadden's random utility model and capable of handling multiple response data.

It was found that not only factors from the classical technological diffusion viewpoint but also factors such as organizational tools and strategic behaviors play an important role in firms' adoption of information systems. Specifically, epidemic effect generally outweighs size effect, and putting more effort into the intensity of information strategy planning is more influential than the hiring of a professional chief information officer. On the other hand, such variables as age of the firm, labor intensity, and number of PCs per person generally have no significant impacts. Finally, a relatively strong complementary relationship exists between enterprise resource planning and customer relationship management adoption, and between e‐buy and groupware adoption.

The results presented in this paper have important implications for firms on a minimal budget that want to maximize their productivity through the adoption of information systems. They also provide important information for government policymakers whose job it is to design strategies for the successful deployment of information systems.

The purpose of this paper is to examine the ability of hedge funds and funds of hedge funds to generate absolute returns using fund level data.

The absolute return profiles are identified using properties of the empirical distributions of fund returns. The authors use both Bayesian multinomial probit and frequentist multinomial logit regressions to examine the relationship between the return profiles and fund characteristics.

Some evidence is found that only some hedge funds strategies, but not all of them, demonstrate higher tendency to produce absolute returns. Also identified are some investment provisions and fund characteristics that can influence the chance of generating absolute returns. Finally, no evidence was found for performance persistence in terms of absolute returns for hedge funds but some limited evidence for funds of funds.

This paper is the first attempt to examine the hedge fund return profiles based on the notion of absolute return in great details. Investors and managers of funds of funds can utilize the identification method in this paper to evaluate the performance of their interested hedge funds from a new angle.

Using the properties of the empirical distribution of the hedge fund returns to classify them into different absolute return profiles is the unique contribution of this paper. The application of the multinomial probit and multinomial logit models in the fund performance and fund characteristics literature is also new since the dependent variable in the authors' regressions is multinomial.

In this paper, we consider the analysis of models for univariate and multivariate ordinal outcomes in the context of the latent variable inferential framework of Albert and Chib (1993). We review several alternative modeling and identification schemes and evaluate how each aids or hampers estimation by Markov chain Monte Carlo simulation methods. For each identification scheme we also discuss the question of model comparison by marginal likelihoods and Bayes factors. In addition, we develop a simulation-based framework for analyzing covariate effects that can provide interpretability of the results despite the nonlinearities in the model and the different identification restrictions that can be implemented. The methods are employed to analyze problems in labor economics (educational attainment), political economy (voter opinions), and health economics (consumers’ reliance on alternative sources of medical information).

This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the composite marginal likelihood (CML) approach in multivariate ordered-response situations. The ability of the two approaches to recover model parameters in simulated data sets is examined, as is the efficiency of estimated parameters and computational cost. Overall, the simulation results demonstrate the ability of the CML approach to recover the parameters very well in a 5–6 dimensional ordered-response choice model context. In addition, the CML recovers parameters as well as the MSL estimation approach in the simulation contexts used in this study, while also doing so at a substantially reduced computational cost. Further, any reduction in the efficiency of the CML approach relative to the MSL approach is in the range of nonexistent to small. When taken together with its conceptual and implementation simplicity, the CML approach appears to be a promising approach for the estimation of not only the multivariate ordered-response model considered here, but also for other analytically intractable econometric models.

In this article we propose a multivariate dynamic probit model. Our model can be viewed as a nonlinear VAR model for the latent variables associated with correlated binary time-series data. To estimate it, we implement an exact maximum likelihood approach, hence providing a solution to the problem generally encountered in the formulation of multivariate probit models. Our framework allows us to study the predictive relationships among the binary processes under analysis. Finally, an empirical study of three financial crises is conducted.

This paper formulates and analyzes Bayesian model variants for the analysis of systems of spatial panel data with binary-dependent variables. The paper focuses on cases where latent variables of cross-sectional units in an equation of the system contemporaneously depend on the values of the same and, eventually, other latent variables of other cross-sectional units. Moreover, the paper discusses cases where time-invariant effects are exogenous versus endogenous. Such models may have numerous applications in industrial economics, public economics, or international economics. The paper illustrates that the performance of Bayesian estimation methods for such models is supportive of their use with even relatively small panel data sets.

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

The search for flexible models has led the simple multinomial logit model to evolve into the powerful but computationally very demanding mixed multinomial logit (MMNL) model. That flexibility search lead to discrete choice hybrid choice models (HCMs) formulations that explicitly incorporate psychological factors affecting decision making in order to enhance the behavioral representation of the choice process. It expands on standard choice models by including attitudes, opinions, and perceptions as psychometric latent variables.

In this paper we describe the classical estimation technique for a simulated maximum likelihood (SML) solution of the HCM. To show its feasibility, we apply it to data of stated personal vehicle choices made by Canadian consumers when faced with technological innovations.

We then go beyond classical methods, and estimate the HCM using a hierarchical Bayesian approach that exploits HCM Gibbs sampling considering both a probit and a MMNL discrete choice kernel. We then carry out a Monte Carlo experiment to test how the HCM Gibbs sampler works in practice. To our knowledge, this is the first practical application of HCM Bayesian estimation.

We show that although HCM joint estimation requires the evaluation of complex multi-dimensional integrals, SML can be successfully implemented. The HCM framework not only proves to be capable of introducing latent variables, but also makes it possible to tackle the problem of measurement errors in variables in a very natural way. We also show that working with Bayesian methods has the potential to break down the complexity of classical estimation.

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.