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Book part
Publication date: 1 January 2008

Arnold Zellner

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making…

Abstract

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Details

Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

Article
Publication date: 4 September 2019

S. Khodaygan and A. Ghaderi

The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit…

Abstract

Purpose

The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.

Design/methodology/approach

In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.

Findings

The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.

Practical implications

The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.

Originality/value

Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.

Details

Assembly Automation, vol. 39 no. 5
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 7 June 2021

Carol K.H. Hon, Chenjunyan Sun, Bo Xia, Nerina L. Jimmieson, Kïrsten A. Way and Paul Pao-Yen Wu

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to…

Abstract

Purpose

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

Design/methodology/approach

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Findings

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

Originality/value

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

Details

Engineering, Construction and Architectural Management, vol. 29 no. 5
Type: Research Article
ISSN: 0969-9988

Keywords

Abstract

Details

Applying Maximum Entropy to Econometric Problems
Type: Book
ISBN: 978-0-76230-187-4

Article
Publication date: 1 March 1993

George T. Duncan

Future developments in methodology have the potential to improve management research and better couple it to management practice. These developments are on six fronts: (1…

Abstract

Future developments in methodology have the potential to improve management research and better couple it to management practice. These developments are on six fronts: (1) computer technology, (2) data capture and experimentation, (3) privacy, confidentiality, and data access, (4) causation, (5) modeling and simulation, and (6) Bayesian statistics. The potential of each is explored, and problems, both technical and administrative, in fulfilling this potential are identified. On the computer and communications front, the key elements are the use of relational database management systems, increased computing power for analysis purposes, and computer networking. On the privacy, confidentiality, and data access front, the key elements are new capabilities for data capture through real‐time surveillance, inferential disclosure threats in computer databases, the demand for more access to detailed data, and public concerns for privacy invasion. Management research is a search for causal mechanisms that can be investigated through empirical studies and that facilitate control of complex processes. In the modeling area, there will be (1) greater use of computing power, (2) less use of model‐independent statistical hypothesis testing, and (3) easier to use computer software for modeling and simulation. The Bayesian perspective of consistently expressing uncertainty through probability distributions will become more widely used in management research.

Details

The International Journal of Organizational Analysis, vol. 1 no. 3
Type: Research Article
ISSN: 1055-3185

Article
Publication date: 4 November 2014

Keith Becker, Jim Sprigg and Alex Cosmas

The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the…

Abstract

Purpose

The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the impact of direct marketing promotions on purchase behavior. However, many modern marketing programs must drive multiple purchase objectives, requiring more precise arbitration between multiple offers and collection of more data with which to differentiate individuals. This often results in datasets that are highly dimensional, yet also sparse, straining the power of statistical methods to properly estimate the effect of promotional treatments.

Design/methodology/approach

Improvements in computing power have enabled new techniques for predicting individual behavior. This work investigates a probabilistic machine-learned Bayesian approach to predict individual impacts driven by promotional campaign offers for a leading global travel and hospitality chain. Comparisons were made to a linear regression, representative of the current state of practice.

Findings

The findings of this work focus on comparing a machine-learned Bayesian approach with linear regression (which is representative of the current state of practice among industry practitioners) in the analysis of a promotional campaign across three key areas: highly dimensional data, sparse data and likelihood matching.

Research limitations/implications

Because the findings are based on a single campaign, future work includes generalizing results across multiple promotional campaigns. Also of interest for future work are comparisons of the technique developed here with other techniques from academia.

Practical implications

Because the Bayesian approach allows estimation of the influence of the promotion for each hypothetical customer’s set of promotional attributes, even when no exact look-alikes exist in the control group, a number of possible applications exist. These include optimal campaign design (given the ability to estimate the promotional attributes that are likely to drive the greatest incremental spend in a hypothetical deployment) and operationalizing efficient audience selection given the model’s individualized estimates, reducing the risk of marketing overcommunication, which can prompt costly unsubscriptions.

Originality/value

The original contribution is the application of machine-learning to Bayesian Belief Network construction in the context of analyzing a multi-channel promotional campaign’s impact on individual customers. This is of value to practitioners seeking alternatives for campaign analysis for applications in which more commonly used models are not well-suited, such as the three key areas that this paper highlights: highly dimensional data, sparse data and likelihood matching.

Details

Journal of Consumer Marketing, vol. 31 no. 6/7
Type: Research Article
ISSN: 0736-3761

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Abstract

Details

Applying Maximum Entropy to Econometric Problems
Type: Book
ISBN: 978-0-76230-187-4

Article
Publication date: 16 March 2010

Leonidas A. Zampetakis and Vassilis S. Moustakis

The purpose of this paper is to present an inductive methodology, which supports ranking of entities. Methodology is based on Bayesian latent variable measurement modeling…

Abstract

Purpose

The purpose of this paper is to present an inductive methodology, which supports ranking of entities. Methodology is based on Bayesian latent variable measurement modeling and makes use of assessment across composite indicators to assess internal and external model validity (uncertainty is used in lieu of validity). Proposed methodology is generic and it is demonstrated on a well‐known data set, related to the relative position of a country in a “doing business.”

Design/methodology/approach

The methodology is demonstrated using data from the World Banks' “Doing Business 2008” project. A Bayesian latent variable measurement model is developed and both internal and external model uncertainties are considered.

Findings

The methodology enables the quantification of model structure uncertainty through comparisons among competing models, nested or non‐nested using both an information theoretic approach and a Bayesian approach. Furthermore, it estimates the degree of uncertainty in the rankings of alternatives.

Research limitations/implications

Analyses are restricted to first‐order Bayesian measurement models.

Originality/value

Overall, the presented methodology contributes to a better understanding of ranking efforts providing a useful tool for those who publish rankings to gain greater insights into the nature of the distinctions they disseminate.

Details

Journal of Modelling in Management, vol. 5 no. 1
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 19 December 2018

Farzana Akbari, Mahdi Salehi and Mohammad Ali Bagherpour Vlashani

The purpose of this paper is to investigate the relationship between tax avoidance, firm value and managerial ability in Tehran Stock Exchange and Over the Counter (OTC)…

1294

Abstract

Purpose

The purpose of this paper is to investigate the relationship between tax avoidance, firm value and managerial ability in Tehran Stock Exchange and Over the Counter (OTC), according to the related theoretical foundations.

Design/methodology/approach

To calculate the managerial ability in this study, DEA is used based on the accounting data, company profile and industry and the hypotheses are estimated in a period of 12 years during 2004 to 2015 in TSE and OTC. Within the previous studies, to test the hypotheses, only the classical regression method is usually used and most of the times the effect of macroeconomic variables is not considered. In this study, in a new act for testing the hypotheses, three statistical methods are used, that is, classical regression models, mixed effects multilevel models and Bayesian multilevel models. Also in this study, the test of structural change is used to control the effects of macroeconomic variables, like inflation and other economic and political influence, on the results.

Findings

The results of these three methods show that the effect of income smoothing and earnings quality on the relationship between tax avoidance and firm value are significant.

Originality/value

Although several studies are conducted so far on the subject of the study, the current study is the first project which combined Bayesian econometrics. Therefore, the results are quite noble.

Details

International Journal of Organizational Analysis, vol. 27 no. 1
Type: Research Article
ISSN: 1934-8835

Keywords

Article
Publication date: 1 March 2022

Bijitaswa Chakraborty, Manali Chatterjee and Titas Bhattacharjee

One of the adverse effects of COVID-19 is on poor economic and financial performance. Such economic underperformance, less demand from the consumer side and supply chain…

Abstract

Purpose

One of the adverse effects of COVID-19 is on poor economic and financial performance. Such economic underperformance, less demand from the consumer side and supply chain disruption is leading to stock market volatility. In such a backdrop, this paper aims to find the impact of COVID-19 on the Indian stock market by analyzing the analyst’s report.

Design/methodology/approach

The sample includes a cross-sectional data set on selected Indian firms that are indexed in BSE 100. The authors calculate the score of disclosure tone by using a textual analysis tool based on the analyst report of selected BSE 100 firms' approach in tackling COVID-19’s impact. The relationship between the tone of the analyst report and stock market performance is examined. This empirical model also survives robustness analysis to establish the consistency of the findings. This study uses both frequentist statistics and Bayesian statistics approach.

Findings

The empirical result shows that tone has negative and significant influence on stock market performance. This study indicates that either analysts are not providing value-relevant and incremental information, which can reduce the stock market volatility during this pandemic situation or investors are not able to recognize the optimism of the information.

Practical implications

This study provides an interesting insight regarding retail investors' stock purchasing behavior during the crisis period. Hence, this study also lays out crucial managerial implications that can be followed by preparers while preparing corporate disclosure.

Originality/value

In the concern on pandemic and its impact on the stock market, this study sheds light on investors' preferences during the crisis period. This study uniquely focuses on analyst reports and investors' preference which has not been studied widely. To the best of the authors’ knowledge, this is the first study in the Indian context, which aims to understand retail investors’ investment preferences during a pandemic.

Details

Journal of Financial Reporting and Accounting, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1985-2517

Keywords

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