Improving an outpatient clinic utilization using decision analysis-based patient scheduling

This study presents a predictive model to be used in scheduling patients in an urban outpatient clinic. The model is based upon actual patient characteristics from a physical therapy clinic within an urban health and wellness center situated in a public university. A number of reported patients' characteristics such as age, education level, distance from the clinic, historical attendance records, etc. were examined to determine if they significantly impacted the patients' missing scheduled appointments (no-shows.) Decision tree analysis was used to develop a model that assessed the likelihood of a patient's no-show, using key patient characteristics and attendance records. Such a model can be used to assist with scheduling patients in an outpatient clinic, while attempting to increase the clinic's overall utilization. Four tree growing criteria were examined to develop the model with the strongest predictive power. Predictive power of each method was assessed by using the entire dataset as well as using split sampling. The results were then compared with those of a Bayesian networks model and a neural networks model. In addition, the trade-off between the selected decision tree model's predictive power versus simplicity of the associated classification rules was examined. We also assessed the impact of various levels of overbooking on the clinic's utilization when using patients' schedules based on the predictive model.     

Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice

We develop a model which reflects the tendency of people to simplify the decision problems they face. The decision maker chooses among alternate strategies only on the basis of the payoff she assesses she would obtain from them, and these assessments do not explicitly take into account her subjective judgements regarding the likelihood of alternate states of the world. At each stage, the decision maker chooses the strategy that she assesses to give the highest payoff. She updates her assessments adaptively. We show that such behavior leads to maxmin choices. We also consider the decision maker who experiences shocks. Journal of Economic Literature Classification Numbers: C7, D8.     

Nonlinear Correlograms and Partial Autocorrelograms*

This paper proposes neural network‐based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.     

The effect of household characteristics on living standards in South Africa 1993–1998: a quantile regression analysis with sample attrition

This paper examines whether the dismantling of apartheid has resulted in an improvement in the standard of living for the vast majority of South Africans. The study is based on a panel data set from the Kwazulu‐Natal province. We use weighted quantile regressions to examine the distribution of standards of living, which corrects for the potential bias arising from non‐random sample attrition. Our results show that there has been a significant increase in the spread of the distribution of household expenditure of the non‐white households residing in Kwazulu‐Natal province. Copyright © 2006 John Wiley & Sons, Ltd.     

Empirical Investigation of the Ability of Sensitivity of Stock Prices to Earnings News in Predicting Earnings Management and Management Forecast Errors

This paper presents an evidence that a firm’s Sensitivity of Stock Price to Earnings News (SSPEN), as measured by surplus stock demand over its supply, affects on incentives to manage earnings and, in turn, Management Forecast Errors (MFE). In particular, we find a tendency for firms rated a Sell (Buy) to engage more (less) frequently in extreme, income-decreasing Earnings Management (EM), indicating that they have relatively stronger (weaker) incentives to create accounting reserves especially in the form of earnings baths than other firms. In contrast, firms rated a Buy (Sell) are more (less) likely to engage in earnings management that leaves reported earnings equal to or slightly higher than management forecasts. The result of empirical evidence from Iranian firms in Tehran Stock Exchange (TSE) showing the existence of a meaningful relationship between SSPEN and EM. Generally, SSPEN can be used to predict EM and Forecast Errors (FEs).     

On the pooling of cross-sectional and time-series data in the presence of heteroskedasticity

We analyze the effect of heteroskedasticity on log-linear aggregation, and its implications for the pooling of cross-section and aggregate time series data. An empirical analysis of food consumption, based on US family budget survey and aggregate time series data, illustrates.     

Agility evaluation in public sector

Agility metrics are difficult to define in general, mainly due to the multidimensionality and vagueness of the concept of agility itself. In this paper, a knowledge-based framework is utilized for the measurement and assessment of public sector＇s agility by A. T. Kearney model. In this research, the authors used Z2 test in SPSS software. So, the authors discovered that the General Office of Standards ＆ Industrial Research of Sistan ＆ Baluchestan Province in Iran is agile. Finally, the authors determine the weakness points of organization by Friedman test.     

Codependent cycles

This paper extends the work of Engle and Kozicki (1993) to test for co-movement in multiple time series when their cycles are not exactly synchronized. We call these codependent cycles and show that testing and estimation in this case will be a Generalized Method of Moments test and estimation procedure. We also show that the Tiao and Tsay (1985) proposed test for scalar components models of order (0, q) can be seen as a test for codependent cycles based on a consistent, but sub-optimal, estimate of the cofeature vector. We assess the small sample performance of the proposed tests through a series of simulations. Finally we apply this test to investigate comovement between durable and non-durable consumption expenditures.     

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.     

The missing link: using the NBER recession indicator to construct coincident and leading indices of economic activity

We use the information content in the decisions of the NBER Business Cycle Dating Committee to construct coincident and leading indices of economic activity for the United States. We identify the coincident index by assuming that the coincident variables have a common cycle with the unobserved state of the economy, and that the NBER business cycle dates signify the turning points in the unobserved state. This model allows us to estimate our coincident index as a linear combination of the coincident series. We compare the performance of our index with other currently popular coincident indices of economic activity.