The role of accounting and the accountancy profession in economic development: A research agenda

A widely held view in the accountancy profession and the donor community is that accounting and the accountancy profession play an essential role in economic development. However, our review of the academic literature finds limited empirical research evidence on the relation between accounting and the accountancy profession and economic development, including specifically whether any such relation is causal, and if so, the direction of the causality. Entities, including those comprising the donor community, policymakers, and professional accountancy organizations (PAOs), need evidence on the question of whether and how the accountancy profession contributes to economic development. Such research could assist donors in evaluating the outcomes of interventions aimed at building the capacity of PAOs in emerging and developing economies and inform future interventions. We summarize the limited academic research addressing the relation between PAOs and economic development and present insights from two roundtables facilitated by the International Association for Accounting Education and Research. We identify research opportunities and research design considerations. We hope our paper will stimulate accounting researchers to advance this literature.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

人工智能将为农机带来什么?

正相较古人,我们生活在这个充满技术进步的时代,可算得上是"特权一代"了。几乎所有事情都要靠自己手动完成的日子已经一去不复返,现在我们生活的世界上,很多工作都可以由机器、软件和各种自动流程接管。之所以有这样的改变,人工智能(AI)功不可没。人工智能是以"像人一样思考和行动"的出发点设计和编程的机器,虽然诞生的时日不长,尚处于萌芽阶段,但已经成为我们日常生活的重要组成部分,并且正在改变我们     

Stochastic Frontier Estimation of Market Power in the Food Industries

We use a modified version of the stochastic frontier model to estimate oligopoly markups above the perfectly competitive frontier, separating out deterministic markups from purely stochastic markups. Using data from 42 US food processing industries between 1990 and 2010, empirical results indicate a widespread incidence of oligopoly power, with Lerner indexes averaging approximately 21%. Further, the estimated markups increase with industrial concentration but decrease with price elasticities and imports. Finally, the estimated Lerner indexes are in the range of previous food industry estimates using New Empirical Industrial Organization (NEIO) models.     

The effect of consumer food creation on the food consumption experience

Two experiments show that the role the consumer plays in the creation of their food can affect the consumption experience. When guilt is present in the consumption of the food item, namely an unhealthy food item, having someone else create the food item increases taste evaluations, attitudes, and behavioral intentions. However, when guilt is not present, as in the case for healthy foods, there is no difference in food evaluations when the end consumer or another creates the food. These results have important implications for food retailers, food service establishment, and food delivery companies.     

Policy capturing in small‐group decision making: Using linear programming to assign factor weights for the NCAA Selection Committee's ranking decisions in the 2016 through 2018 men's basketball tournaments

We present a general approach for applying policy capturing to small‐group decision making and demonstrate the approach with the NCAA Selection Committee's rankings of the 68 teams in the three most recent Division I Men's Basketball Tournaments. We develop a linear programming model that minimizes the extent to which the evaluation of a lower‐ranked team exceeds that of a team ranked immediately above it. The result is a set of linear weights that approximates a group decision when the decision itself is demonstrably internally inconsistent in that a group ranks some teams higher than others despite a lower implied evaluation.     

Family business goal formation: a literature review and discussion of alternative algorithms

Management Review Quarterly - Goals represent an organization’s desired outcomes, and family businesses are known to pursue multiple goals, which commonly include both financial and...