Search results

1 – 10 of over 3000
To view the access options for this content please click here
Book part
Publication date: 20 September 2021

Ke Gong and Scott Johnson

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was…

Abstract

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.

To view the access options for this content please click here
Article
Publication date: 7 April 2021

Guohua Cao and Jing Zhang

This study aims to combine two fraud-related streams of the literature on guanxi and overconfidence into an integrated framework, which is the fraud triangle, to interpret…

Abstract

Purpose

This study aims to combine two fraud-related streams of the literature on guanxi and overconfidence into an integrated framework, which is the fraud triangle, to interpret the mechanism of fraud commission and detection.

Design/methodology/approach

A bivariate probit model with Partial Observability (POBi Probit) is applied. Moreover, the POBi Probit model is adjusted to the Chinese context. The China-specific POBi Probit model is constructed using data of Chinese A-share listed companies from 2008 to 2014, with a total of 15,109 firm-year observations.

Findings

Overconfidence induces fraud commission and worsens fraud detection; overconfidence mediates the relationship between fraud and guanxi; the “white side” of guanxi comes from alumni networks, while the “dark side” is derived from relatives-based networks; overconfidence induces fraud commission in accounting and disclosure and benefits the detection of disclosure frauds. Guanxi suppresses fraud commission in management and disclosure, however, it worsens fraud detection given fraud in management and disclosure; overconfidence induces fraud commission in both state-owned enterprises (SOE) and non-SOEs, and benefits fraud detection in SOEs. Guanxi suppresses fraud commission and worsens fraud detection in SOEs and city-owned firms.

Research limitations/implications

There are two drawbacks of the partial observable bivariate probit (POBi-Probit) method that must be mentioned here. On one hand, the ex ante variable selection is one of the most difficult parts of applying the POBi-Probit model and different variables are included in different studies. On the other hand, the POBi-Probit model might not converge if too many variables are included. Thus, many widely accepted factors can be included in the model. Thus, this study initially sets the POBi-Probit model based mainly on Khanna et al. (2015) and then adjusts the model for the Chinese context (e. g. considering government ownership) according to Yiu et al. (2018) and Zhang (2018) and the local study of Meng et al. (2019). Considering the observability of fraud, on one hand, the observability of fraud commission is a widely accepted limitation, especially when accounting opacity comes across with regulatory efficiency (Yiu et al. (2018). On the other hand, the observability of relationships is another obstacle to this study. Future studies can go further by revealing the presently unobservable relationships using Big Data technology.

Originality/value

This paper theoretically and practically contributes to the literature on both corporate fraud and corporate governance. Theoretically, by introducing integrated principal-agent resource-reliance theory (IPRT) and upper echelon theory (UET), this paper broadens the framework of fraud triangle theory (FTT) and testifies the availability of the broaden FTT in the transitional and emerging-market context of China. Practically, this paper provides evidence that guanxi and overconfidence are two of the factors affecting corporate fraud. Thus, this paper provides a governance approach opposing corporate fraud in China, which may help the other emerging economies in transition.

Details

Chinese Management Studies, vol. 15 no. 3
Type: Research Article
ISSN: 1750-614X

Keywords

To view the access options for this content please click here
Article
Publication date: 17 February 2012

Deniz Tudor and Bolong Cao

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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

To view the access options for this content please click here
Article
Publication date: 1 April 2001

Clarence N.W. Tan and Herlina Dihardjo

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial…

Abstract

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

Details

Managerial Finance, vol. 27 no. 4
Type: Research Article
ISSN: 0307-4358

Keywords

To view the access options for this content please click here
Book part
Publication date: 1 December 2016

R. Kelley Pace and James P. LeSage

We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK…

Abstract

We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK (Geweke-Hajivassiliou-Keane) algorithm to perform maximum simulated likelihood estimation. However, using the GHK for large sample sizes has been viewed as extremely difficult (Wang, Iglesias, & Wooldridge, 2013). Nonetheless, for sparse covariance and precision matrices often encountered in spatial settings, the GHK can be applied to very large sample sizes as its operation counts and memory requirements increase almost linearly with n when using sparse matrix techniques.

Details

Spatial Econometrics: Qualitative and Limited Dependent Variables
Type: Book
ISBN: 978-1-78560-986-2

Keywords

To view the access options for this content please click here
Book part
Publication date: 13 December 2013

Bertrand Candelon, Elena-Ivona Dumitrescu, Christophe Hurlin and Franz C. Palm

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…

Abstract

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.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

Keywords

To view the access options for this content please click here
Book part
Publication date: 1 December 2016

Roman Liesenfeld, Jean-François Richard and Jan Vogler

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian…

Abstract

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

Details

Spatial Econometrics: Qualitative and Limited Dependent Variables
Type: Book
ISBN: 978-1-78560-986-2

Keywords

To view the access options for this content please click here
Article
Publication date: 3 August 2012

Sotiris Tsolacos

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of European Real Estate Research, vol. 5 no. 2
Type: Research Article
ISSN: 1753-9269

Keywords

To view the access options for this content please click here
Article
Publication date: 1 March 1991

Hian Chye Koh and Robert Moren Brown

This article discusses the limitations of existing going‐concerndiscriminant models and explores the use of weighted probit analysis toconstruct a classification model for…

Abstract

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.

Details

Managerial Auditing Journal, vol. 6 no. 3
Type: Research Article
ISSN: 0268-6902

Keywords

To view the access options for this content please click here
Article
Publication date: 1 February 1998

Michael J. Peel, Mark M.H. Goode and Luiz A. Moutinho

This paper reviews the use of logit and probit models in marketing and focuses on demonstrating the use of ordered probability models. This type of model is appropriate…

Abstract

This paper reviews the use of logit and probit models in marketing and focuses on demonstrating the use of ordered probability models. This type of model is appropriate for many applications in marketing and business where the dependent variable of interest is ordinal (e.g., likert scales). A comparison between the properties of the ordinary least squares (OLS) model and ordered logit and probit models is made using consumer satisfaction data on automobiles. This comparison between the two models shows that the use of OLS for ordered categorical data gives misleading results and produces biased estimates, leading to inaccurate hypothesis testing. The paper concludes that ordered probability models, such as the ones illustrated, should be employed in marketing and business research where the dependent variable is ordinal.

Details

International Journal of Commerce and Management, vol. 8 no. 2
Type: Research Article
ISSN: 1056-9219

1 – 10 of over 3000