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This article discusses the limitations of existing going‐concern discriminant models and explores the use of weighted probit analysis to construct a classification model for the auditor to use in making going/non‐going concern decisions. The model is constructed using probit analysis with the weighted exogenous sample maximum likelihood (WESML) procedure on a matched sample of 80 going and non‐going concerns. Using the Lachenbruch′s U method of computing holdout accuracy rates, the probit model classifies going/non‐going concerns with an accurate rate of 82.50 per cent for non‐going concerns, 100.00 per cent for going concerns, and 91.50 per cent overall. These accuracy rates are higher than those of the sample auditors, which are 40.00, 97.50, and 68.75 per cent, respectively. The model is expected to be useful as a going‐concern prediction model, a persuasive analytical tool, and a defensive device.

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.

The purpose of this paper is to assess the behaviour of economic sentiment indicators at rent‐growth turning points and indicators' ability to forecast such turning points. More specifically, the paper looks at whether early signals are generated for forthcoming periods of negative and positive office rent growth. The analysis aims to complement structural model forecasting in the real estate market with short‐term forecasting techniques designed to predict turning points.

The objective of this study is achieved by deploying a probit model to examine the ability of economic sentiment indicator series to signal the direction of office rents and the strength of movement in this direction. The main advantage of this approach is that it is geared towards predicting turning points. Probit models are non‐linear in nature, and as such they can capture more effectively the likely asymmetric adjustments when turning points occur than linear methodologies would. The analysis is applied to three major office centres – La Défense, London City, and Frankfurt – to examine whether the results will differ by geography.

The findings reveal that the probit methodology utilising information from economic sentiment indicators generates advance signals for periods of contraction and expansion in office rents across all three markets: La Défense, London City, and Frankfurt. The lead times for La Défense and Frankfurt are longer than those for London City and range between three and nine months. The evidence in this paper clearly supports the appeal of sentiment indicators and probit analysis to inform forecasting and risk assessment processes.

Acknowledging the limitations of structural models and related methodologies and the lack of adequate research on turning‐point prediction in the real estate market, this study forecasts episodes of negative and positive office rent growth applying appropriate techniques and data that lead economic activity, are of monthly frequency, and are not revised historically. The paper raises awareness of a forecasting approach that should complement structural models and judgmental forecasting, given its suitability for short‐term forecasting and for signalling turning points in advance.

This chapter provides a statistical analysis of the determinants of attitudes toward foreigners displayed by Europeans sampled in Eurobarometer surveys in 1988 and 1997. Those who compete with immigrants in the labor market are more negative toward foreigners. An increased concentration of immigrants in neighborhoods increases the likelihood of negative attitudes. Racial prejudice exerts a strong influence on anti-foreigner sentiment. Greater racial prejudices, and the decline in the strength of educational attainment in reducing negative attitudes toward foreigners, contribute to the increased anti-foreigner attitudes between 1988 and 1997.

This paper uses a probit model to analyse 100 observations in terms of three hypotheses about the formation of owners’ corporations in high‐density private housing estates in Hong Kong within the context of Mancur Olson’s group theory. The findings do not reject the theory, revealing that it is more likely for an older urban estate with fewer owners to form owners’ corporations. The discussion includes a brief introduction to Olson’s group theory and the development of the probit analysis. Some speculative thoughts about public participation in local level urban management and planning are offered in the conclusion.

The purpose of this paper is to explore the relationship between changes in the business cycle (as indicated by the incidence and duration of unemployment) and the incidence of suicide.

A theoretical utility model with savings and consumption is used, while time series micro‐level suicide data and probit analysis is used to empirically test the implications of the model.

With declining economic activity and the corresponding increase in unemployment the propensity to commit suicide rises among men for numerable reasons. The authors hypothesize that there is a negative impact with respect to the decline in economic activity and as the intensity increases with respect to the declining business cycle, female's suicides will tend to accelerate.

One of the primary limitations of this study is the amount of control variables to which the authors had access. There are many factors that would influence an individual when determining whether or not to take their own life. Religious convictions, the presence of children, income, educational attainment, occupational attainment, pre‐unemployment income, and how long one had been married or divorced (or unmarried) are all variables that could influence the likelihood of a suicide. The center of disease control (CDC) public use files, however, do not include these variables; thus, the authors were unable to control for their impact.

The authors believe that these findings merit greater public awareness and increases in various forms of public and private support for recently unemployed individuals, being particularly attentive to the effect of higher than normal rates and durations of unemployment and the differences based on gender. These findings also establish another sound rationale for public policies to encourage the increase of personal savings during times of employment to make weathering periods of unemployment easier.

In times of increased incidence and duration of unemployment, the tendency of legislators and other public policy makers presumably would be to establish programs targeted to address the population with the highest rates of unemployment‐related suicide – White males. It can be argued, however, that since the increased incidence and duration of unemployment have a greater effect on increasing the rate of suicide in women, public policies and programs targeting the specific needs and issues of those unemployed women with an increased risk of suicide would be more cost‐effective, preventing or reducing those incremental suicides and mitigating their negative economic, social, and familial impacts.

Previous studies used descriptive statistics, contingency tables, and the traditional statistical regression techniques in their empirical analysis; this study deviates from the norm by the use of probit analysis. Using the probit technique allowed the authors to focus their analysis on the probabilities of suicide with regard not just to the business cycle itself but also to the intensity of the business cycle.

This study aims to investigate the association between Muslim individuals’ portfolio allocation choice and Islamic religiosity (levels and dimensions), controlling for risk tolerance and sociodemographic factors.

The study uses primary data collected via survey questionnaires from a sample of 751 Muslim working individuals in Kuala Lumpur, Malaysia. Owing to the ordinal nature of the dependent variable, which reflects the levels of proportions of risky assets in portfolios, the data were analyzed using an ordered probit regression model.

The findings reveal that Islamic religiosity levels in general were insignificantly related to portfolio allocation, but that two dimensions of religiosity (virtue and obligation) significantly impact the allocations of risky assets in the portfolio. The higher the level of virtue, the lower the propensity to allocate risky assets into the portfolio. On the contrary, the higher the level of obligation, the higher the propensity to allocate risky assets in the portfolio. Meanwhile, individuals with higher risk tolerance, income and education levels show greater propensity to allocate risky assets in the portfolio.

The sample is restricted to Muslims in Kuala Lumpur; hence, the findings are not easily generalized to Muslim investors in general. Findings may differ between Muslims across the world, so future research needs to expand from a country specific to an international analysis. In addition, future studies could include other determinants of portfolio allocation, such as financial literacy.

The findings of this study may assist financial planners and policymakers to better understand the drivers of portfolio allocation among their Muslim clients.

While other studies have tended to focus on the impact of religiosity on the holdings of specific financial assets, such as Islamic bank accounts or Takaful, the present study explores the effect of Islamic religiosity dimensions on the allocations of risky assets in the portfolio. The study also develops an ordinal measure of portfolio allocation and makes a methodological contribution by using an ordered probit regression analysis.

This paper explored factors that impact insurance choices of demand (farmers) and supply (insurance companies) side, respectively.

Specially designed survey questions allow one to fully observe the demand tendency from farmers and partially observe the supply tendency from insurance companies. Using bi‐vairate probit model, a joint estimation of insurance decisions of both supply and demand sides suggested that factors perform different roles in affecting insurance participation.

Farmer's age and education have positive impacts on insurance demand, but are indifference to insurance providers. Insurance suppliers care about farmers' experience in the fields when providing insurance services, however, on the demand side, farmers' experience occasionally results in overconfidence and hence, impedes farmers' insurance purchasing. Production scales, proxy by sow inventory, are put more weight by farmers than insurance suppliers when making decisions. Production efficiency measures perform as incentives for farmers to purchase insurance. While suppliers prefer customers who use vaccine, farmers tend to treat vaccine as a substitute for insurance to prevent disease risk.

Results from bi‐vairate probit model offer deeper understandings about livestock insurance choices and provide further insights to improve policy design and promote participation.

The study designed a special questionnaire and firstly used bi‐vairate probit model to offer more understandings about demand and supply sides of livestock insurance.

This paper investigates the socioeconomic factors which influence Saudi women's decisions to participate in the labour market. A sample of 1225 Saudi women was studied using the logit, probit and discriminant analysis.

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).