Revenue Prediction Research Articles

Preferences for funding particular government services from different taxes

Five studies investigated New Zealanders’ preferences for funding particular government-provided services from different types of tax: flat and progressive income tax and flat consumption tax. Respondents preferred progressive to flat income tax when the service was relatively cheap, and when it was perceived that rich people benefited more from the service. They preferred to fund more expensive services and those perceived as more valuable or vital from income rather than consumption tax, regardless of whether the income tax was flat or progressive. The relative funding preferences for income and consumption tax were correlated with the perceived relative predictability of revenue from the two taxes and the perceived likelihood that the government might change them, but not to respondents’ ideas of how much revenue is obtained from them.

Drug pricing model in a pediatric hospital

The continued growth of health care expenditures demands that pharmacy leadership be vigilant about changes in the pharmacy environment. Providers need to be more aware of the risks and liabilities associated with increasing prescription drug expenditures, labor shortages, reimbursement rates, and fluctuations in patient acuity that affect drug use. These and other factors should be considered during the development of a pharmacy pricing model. Pharmacy management software and other information technologies are powerful tools for designing and implementing pricing models that need to be adaptable and easily updated. Final revenue prediction and model performance must be verified as accurate, fair, and consistent. Children’s National Medical Center (CNMC) is a 279-bed pediatric tertiary-care facility in Washington, D.C. The department of pharmacy services at CNMC receives over 750 physician orders and dispenses more than 3000 doses daily. In 2003, CNMC executive and pharmacy leadership evaluated the methods used to establish patient drug charges. We considered a variety of industry and environmental factors that influence drug costs and revenues. We concluded that the historical pricing method failed to accurately incorporate all expenses associated with dispensing medications to patients in the hospital and at CNMC regional outpatient centers. As a result, the revenue from drug charges did not reliably and consistently cover the costs of drug purchase, storage, preparation, and dispensing.

Forecasting Automobile Demand for Economies in Transition: A Dynamic Simultaneous-Equation System Approach

The dynamic characteristics of automobile demand are critical for national economic and revenue predictions. Automobile demand and ownership level forecasts are also the basis for travel demand models, land-use-transport interaction models, and transport policies and regulations. In this article, a dynamic automobile demand simulation model is developed utilizing a simultaneous-equation system. The system considers the interaction between supply and demand and the resulting equilibrium. Although forecasting automobile demand has been previously investigated, it has not been within such a dynamic simulation framework. Our model includes the current and lagged automobile quantity and price variables; economic, financial and operating cost variables; and income and government policy variables. The capabilities of the model are then demonstrated through performing a number of simulation experiments considering various growth-development scenarios, changes in operating costs, government policies towards automobile imports, and demographic/employment shifts. Relevant tests are applied to examine the econometric specifications and to evaluate the simulation model performance.

Back to Basics: Forecasting the Revenues of Internet Firms

In light of the importance of revenues in the valuation of internet stocks, this paper examines the roles played by analysts, past revenues, and web usage data (unique visitors, pageviews, and minutes spent at a firm's web sites) in the forecasting of future revenues. In contrast to evidence from other industries, we find that analysts' revenue forecasts almost always underestimate the revenues of internet firms. Historical revenue growth is shown to have incremental predictive power over analysts' forecasts, more so for our sample of portal and content/community firms than for our e-tailer sample. Estimates of web usage growth, on the other hand, generally have significant incremental value for predicting the revenues of the e-tailers, but little predictive power for the revenues of the p/c firms. Finally, we examine whether measures of web traffic have incremental value in the prediction of revenues above time-series forecasts. This issue is especially important when valuing firms with little or no analyst coverage, where there is, of necessity, an increased reliance on historical revenues for forecasting purposes. We find that all three web usage metrics do have significant incremental predictive power.

Back to Basics: Forecasting the Revenues of Internet Firms

In light of the importance of revenues in the valuation of internet stocks, this paper examines the roles played by analysts, past revenues, and web usage data (unique visitors, pageviews, and minutes spent at a firm's web sites) in the forecasting of future revenues. In contrast to evidence from other industries, we find that analysts' revenue forecasts almost always underestimate the revenues of internet firms. Historical revenue growth is shown to have incremental predictive power over analysts' forecasts, more so for our sample of portal and content/community firms than for our e-tailer sample. Estimates of web usage growth, on the other hand, generally have significant incremental value for predicting the revenues of the e-tailers, but little predictive power for the revenues of the p/c firms. Finally, we examine whether measures of web traffic have incremental value in the prediction of revenues above time-series forecasts. This issue is especially important when valuing firms with little or no analyst coverage, where there is, of necessity, an increased reliance on historical revenues for forecasting purposes. We find that all three web usage metrics do have significant incremental predictive power.

Timber harvest scheduling with price uncertainty using Markowitz portfolio optimization

Harvest scheduling models need to account for uncertain revenue predictions when minimizing risk of financial loss is an important management objective. In this paper, we present methods for estimating the means and covariances of stumpage prices and incorporating them in harvest scheduling models. We approached the estimation problem by fitting time-series models to loblolly pine sawtimber and pulpwood stumpage prices in Georgia, USA, and deriving formulas for means and covariances of price predictions. Statistical evidence supported integrated autoregressive models, which caused covariances of price predictions to increase with time. The means and covariances of price predictions were combined with timber yield and land value predictions to give exact formulas for the revenue means and covariances of timber management activities. Sawtimber regimes dominated pulpwood regimes by providing higher mean revenues across a wide range of revenue variances. Harvest scheduling results for a hypothetical forest of pine plantations showed that the forest plan that maximized mean income without concern for risk (expressed as the standard deviation of income) involved sawtimber production with a 35-year rotation age. Risk was reduced 30% with little effect on mean income by using shorter-rotation sawtimber regimes. Risk was reduced 80% by using a mix of short-rotation sawtimber and pulpwood regimes because pulpwood price was only weakly correlated with sawtimber price. The latter risk-reduction came at the expense of mean income, which was reduced by as much as 50%. The risks and compositions of optimal forest plans were extremely sensitive to assumptions about the range of future prices that were inherent in different prediction models. This sensitivity emphasizes the importance of carefully determining the decision maker’s beliefs about stumpage price behavior.

SilverScreener: A Modeling Approach to Movie Screens Management

Managing the allocation of shelf space for new products is a problem of significant importance for retailers. The problem is particularly complex for exhibitors—the retailers in the motion picture supply chain—because they face dynamic challenges, given the short life cycles of movies, the changing level of demand over time, the scarcity of shelf space, and the complex revenue sharing contract between the exhibitor and the distributor. In the face of this complexity, the aim of current research is to provide a structure for analyzing management problems of exhibitors in the movie industry. Using a mathematical programming approach and a fast, but readily accessible algorithm, we propose a decision support model, SilverScreener, whose aim is to help exhibitors make effective and timely decisions regarding theater screens management. The major objective is to help select and schedule movies for a multiple-screens theater over a fixed planning horizon in such a way that the exhibitor's cumulative profit is maximized. By treating the multiple screens as parallel machines and the movies as jobs, we provide an analogy of the current problem to the parallel machine scheduling problem. We formulate the resulting problem as an integer program. We depart from the typical parallel machine scheduling problems by introducing the time-indexed formulation that is particularly useful for solving the current problem. An important distinction between the current problem and typical machine scheduling problems is that the present approach allows for the choice of which movies to play; typically, in machine scheduling, all jobs have to be scheduled. We provide various analyses of normative versus actual decision making, based on publicly available data. The developed model is readily implementable and appears to lead to improved profitability in different comparative cases. Through sensitivity analysis, we demonstrate that the above results are robust to variations in various parameters of the problem. The main findings and insights from the normative policy suggest the following: • Based on SilverScreener's recommendations, the exhibitor can achieve substantially higher cumulative profit. • The improvement over actual decisions in terms of profitability appears to result from a combination of better selection and scheduling of the movies. • The general structure of the exhibitor's normative decision is: choose fewer “right” movies and run them longer. We propose a two-tier integrated application of the model to show how the model can be applied to realistic decision making. The first tier involves development of a movie selection plan to help the manager plan an entire season and bid for movies before the start of that season. An ex ante revenue prediction scheme is developed, based intuitively on a matching of the forthcoming movies with similar movies played in this theater previously. If the forthcoming season's scheduling plan can be visualized as a two-dimensional (week-by-screen) matrix, then that matrix contains only “empty cells” before the first tier. After a bid plan is developed, the exhibitor can “fill” some of those empty cells. The remaining empty cells represent slots, which can be decided during the season by either extending movies the exhibitor booked before the season or by scheduling other movies which may become available later in the season. This motivates the second tier—adaptive scheduling approach—of the integrated approach. The second tier helps the exhibitor in weekly decision making during the season. This application involves “rolling,” and updating data, from one time window to another. The approaches followed in the two tiers of the integrated application are quite general in that they can incorporate a sophisticated demand prediction model, managerial judgments, or a combination of both. We also propose an alternative behavioral decision rule (heuristic), which exemplifies relationship dilemmas in the movie industry. This heuristic shows that the exhibitors need to be selective in their choice of movies and may suffer a substantial loss in profitability if they place too much emphasis on accommodating distributors.

Bayesian updating in hierarchic Markov processes applied to the animal replacement problem

The observed level of milk yield of a dairy cow or the litter size of a sow is only partially the result of a permanent characteristic of the animal; temporary effects are also involved. Thus, we face a problem concerning the proper definition and measurement of the traits in order to give the best possible prediction of the future revenues from an animal considered for replacement. A trait model describing the underlying effects is built into a model combining a Bayesian approach with hierarchic Markov process in order to be able to calculate optimal replacement policies under various conditions. Copyright 1993 by Oxford University Press.

Plant Production Cost-accounting/Management System

A microcomputer program has been developed to keep records on energy, labor costs, product pricing, and revenue predictions for greenhouse and nursery production. The program manages plant production data, potentially enabling the grower to improve production and profits. The grower can use the program to determine how much it costs to produce individual plants, to ascertain labor costs and where to reallocate employees. Advertising and other indirect costs can be included to determine cost of production on a per-plant or per-square-foot basis.

Turning Point Analysis: Using Survey Research to Call Changes in Consumer Behavior and Predict Sales Taxes

Forecasters often disagree on revenue predictions because of differing techniques. Some states, such as Utah, have turned to the use of survey research methods to avoid this dilemma. This article reviews many of the national forecasting models examining consumer sentiment currently in use. And it compares the Utah experience with surveys used in other states.

Estimation Accuracy of Sample Survey Methods for Telephone Revenue Prediction

Estimation Accuracy of Sample Survey Methods for Telephone Revenue Prediction

A Sampling Design for Telephone Revenue Prediction

A Sampling Design for Telephone Revenue Prediction

A sociobioeconomic model: The Texas inshore shrimp fishery

A sociobioeconomic model (SBM) of the Texas inshore shrimp fishery is developed an an extension of a General Bioeconomic Fisheries Simulation Model (GBFSM) for annual crop fisheries. The SBM is a heuristic model which redefines the traditional concept of a vessel class to include social and cultural variables that describe the vessel operators. Sociocultural variables that are hypothesized to affect harvesting capability of fishermen, or relative fishing power ( rfp) of the vessels that they operate, are identified and referred to as Fishing Advantage variables. Fishing Advantage variables, age of operator, years of experience, and innovativeness, are quantified and incorporated into the GBFSM through modification of an equation which calculates rfp of different SBM vessel classes. Sociocultural variables hypothesized to affect decisions of vessel operators to exert fishing effort, or nominal days fished ( ndf), are identified and referred to as Motivational variables. These include deferred gratification orientation, work orientation, and vessel ownership status of the vessel operator. A method using decision trees to direct decision-making based on Motivational variables and economic feedback is incorporated into the GBFSM to adjust ndj by different vessel classes. Five idealized inshore vessel classes representing different types of Texas inshore shrimp vessels, whose operators differ in Fishing Advantage and Motivation, are included in the SBM. SBM predictions of landings, effort, revenue, and rent under baseline and two different inshore management policies, an open season inshore policy and a closed season inshore policy, are (1) compared with GBFSM predictions under the same conditions, (2) compared across management policies, and (3) compared between each inshore vessel class. Predictions of inshore landings, effort, revenue, and rent were not significantly ( P > 0.05) different between models under any policy conditions. SBM predictions of inshore landings, effort, revenue, and rent were significantly different ( P < 0.05) between policies. Under an open inshore policy total inshore landings and effort increased relative to baseline, resulting in a 38% increase in rent to the inshore fishery. Under a closed inshore policy total inshore landings and effort decreased, resulting in a 92% decrease in rent to the inshore fishery Examination of the performance of individual inshore vessel classes indicated that policy changes have different economic impacts on different groups of fishermen, with revenue and rent of the lowest producers being most sensitive to policy changes. Explicit representation of sociocultural variables in bioeconomic models of commercial fisheries differentiates performance of different fishermen. It thereby illustrates effects of different management policies on those fishermen more clearly than is possible using models in which only the ‘average’ fisherman is represented.

Planning of Concessions in Airport Terminals

Concessions generate a fair amount of revenue and are an important key to the financial operation of today's large airports. Location and size are the most important factors that affect concession revenues. To check the validity of a location, the accessibility and visibility of concessions are measured by subjective rating and visibility indices, respectively. Good location along major traffic patterns aids the flow and improves the efficiency of the terminal, whereas a poor location can disrupt the smooth flow inside the terminal. Models used for prediction of revenues from concessions are good as far as a particular airport is concerned. The utilization of the market by concessions can be measured in terms of percentage of idle time that people spend in concessions. The Canadian airport survey shows that only 25% of this idle time is spent in concessions, and the rest is spent waiting. Good planning of concessions requires that planning and design be integrated into the overall planning process of the ...