Making sense of complex data using interactive data visualization

An important role for accountants today is to provide decision support to senior management by assisting them in the analysis of large, complex data sets. Interactive data visualization (IDV) facilitates this process by allowing users to navigate, select, and display data via an easy-to-use interface often used as a component of data analytics. Given the increasing popularity of IDV as a tool for making sense of complex data, it is important that accountants become familiar with and learn how to use this technology. This case provides a hands-on opportunity to organize complex accounting data to create IDVs for decision makers to use. Further, the case enables students to understand the potential impact of IDVs on preparers and users of accounting information. Students will assume the role of a division controller in a hypothetical company and create an IDV to assist the chief executive officer (CEO) in decision making.     

Tax avoidance and ex ante cost of capital

This study examines the association between tax avoidance and ex ante cost of equity capital. Based on prior research, we develop two proxies for investors’ expectations of tax avoidance and explore whether deviations from those expectations result in higher ex ante cost of equity capital. We find that the ex ante cost of equity capital increases with tax avoidance that is either below or above investor expectations and that the increase is larger for tax avoidance that exceeds investors’ expectations. We then examine whether firms that alter their future tax avoidance exhibit a lowering of their ex ante cost of equity capital and find that tax avoidance decreases (increases) from the prior year for firms that were above (below) investors’ expectations in the prior year. These results are consistent with the trade‐off suggested by the Scholes and Wolfson framework and reinforce the notion that balancing tax benefits and non‐tax costs is an important feature of firms’ tax planning.     

The Discriminating Beta: Prices and Capacity with Correlated Demands

Uniform customer‐class pricing can do much of the work of congestion‐based or time‐of‐day pricing in communication or wireless networks. A monopolist exploits differences in the stochastic characteristics of demands. If demands are correlated and the firm faces a capacity constraint, then it can set prices to reduce the variability of aggregate demand, thereby reducing the probability of excess demand and the associated service quality deterioration. Demands that covary negatively with aggregate demand are valuable to the firm in much the same way that securities that covary negatively with the market are valuable in a stock portfolio. Customer classes that exhibit low covariance with aggregate demand realize lower optimal prices. Optimal capacity is also affected by these covariances. As long as demands are not perfectly positively correlated, expected costs of joint production are less than expected costs of serving demands separately.     

Forecasting technology diffusion: a new generalisation of the logistic model

The present research proposes a new generalisation of the logistic model aiming at technology diffusion forecasting. Regarding criticisms and failures reported in the literature to apply logistic function for long-term forecasting, in our work we focused on short-term accuracy of forecast. To formulate the model, based on mathematical approximation, at first the differential equation governing the diffusion process is found and then by solving the derived differential equation, the forecast function is obtained. In all steps, mathematical tools from numerical analysis are used. We compared the New Generalized Logistic Model with eight of the most renowned models in the literature. The model led to more accurate fits and forecasts than those obtained from other models we applied for comparison.     

Estimating the gravity model when zero trade flows are frequent and economically determined

ABSTRACT

Since the 1950s, we have known that the presence of zero-valued dependent variables can seriously bias econometric estimates whether the zeros are included or excluded. Yet the widely-used gravity model is frequently estimated on samples that include large fractions of zeros. An influential paper by Santos Silva and Tenreyro – based on simulations that include no economically-determined zeros – concludes that the bias problems resulting from zeros and those resulting from heteroscedasticity and nonlinearity can be solved using the Poisson Pseudo-Maximum-Likelihood (PPML) model including the zero values. This paper begins by adapting the Santos Silva and Tenreyro experimental design to include economically-determined zeros to see whether this conclusion continues to hold. With this design, it finds that alternative estimators have lower bias than PPML. Changing to a Monte Carlo design that replicates the much-higher real-world frequency of predicted values near zero restores the finding of lower bias with the PPML estimator. The results highlight the need for very careful design of Monte Carlo experiments when evaluating alternative estimators of the gravity model.