Wilpen Gorr

A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis

Abstract This paper reports the results of an experiment with simulated data that compares the estimation accuracy of two simple and very different production frontier methods: corrected ordinary least squares and data envelopment analysis. The experimental design extends a previously published paper by introducing measurement errors, a factor we show to be critical for comparative analysis of the frontier methods. Both low and high measurement error distributions are used, resulting in 95% error intervals of roughly ± 10% and 40%, respectively, of outputs. Other variations include four inefficiency distributions covering a wide range of behavior; four sample sizes, from 25 to 200, and two piecewise Cobb-Douglas technologies with two inputs and one output each. Results include: 1) selection of the proper estimation method for a case can result in substantial gains in estimation accuracy for technical efficiencies, from 15 to 40% in mean absolute deviations; 2) COLS performs better for the classical distribution case with sample sizes of 50 or over; 3) DEA performs better for all nonclassical inefficiency distributions, even with relatively high measurement errors; 4) DEA provides surprisingly accurate estimates for the small sample size of 25, for all cases in the experiment; 5) COLS fails to decompose deviations into efficiency and measurement error components (it assumes that deviations from the frontier are either totally due to measurement errors or technical inefficiencies); and 6) neither method performs satisfactorally for high measurement errors.

Political and organizational influences on the accuracy of forecasting state government revenues

Abstract This paper tests a general theory of the factors influencing the accuracy of state government revenue forecasts. Besides the more familiar hypotheses on forecasting techniques and randomness of dependent variable time series, our theory includes hypotheses on the political environment and organizational procedures used in forecasting. The primary data are from three surveys of state governments and include percentage forecasts errors for total and sales tax revenues. The analysis uses two measures of forecast accuracy, the mean and median absolute percentage errors. These are estimated in a linear model that uses ordinary least squares and least absolute value regressions. The results confirm most parts of the theoretical model, subject to the caveats of field data. Forecast accuracy increases when there are independent forecasts from competing agencies. It increases even more when formal procedures exist to combine competing forecasts. It decreases when outside expert advisors are used and when there is a dominant political party or ideology. Finally, it increases when simple regression models and judgmental methods are used as opposed to univariate time series methods or econometric models.

Short-term forecasting of crime

Abstract The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naive methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.

Introduction to crime forecasting

Abstract This short paper introduces the six papers comprising the Special Section on Crime Forecasting. A longer title for the section could have been “Forecasting crime for policy and planning decisions and in support of tactical deployment of police resources.” Crime forecasting for police is relatively new. It has been made relevant by recent criminological theories, made possible by recent information technologies including geographic information systems (GIS), and made desirable because of innovative crime management practices. While focused primarily on the police component of the criminal justice system, the six papers provide a wide range of forecasting settings and models including UK and US jurisdictions, long- and short-term horizons, univariate and multivariate methods, and fixed boundary versus ad hoc spatial cluster areal units for the space and time series data. Furthermore, the papers include several innovations for forecast models, with many driven by unique features of the problem area and data.

Forecasting Analogous Time Series

Organizations that use time-series forecasting regularly, generally use it for many products or services. Among the variables they forecast are groups of analogous time series (series that follow similar, time-based patterns). Their covariation is a largely untapped source of information that can improve forecast accuracy. We take the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. In using Bayesian pooling, we use data from analogous time series as multiple observations per time period in a group-level model. We then combine estimated parameters of the group model with conventional time-series-model parameters, using so-called weights shrinkage. Major benefits of this approach are that it (1) requires few parameters for estimation; (2) builds directly on conventional time-series models; (3) adapts to pattern changes in time series, providing rapid adjustments and accurate model estimates; and (4) screens out adverse effects of outlier data points on time-series model estimates. For practitioners, we provide the terms, concepts, and methods necessary for a basic understanding of Bayesian pooling and the conditions under which it improves upon conventional time-series methods. For researchers, we describe the experimental data, treatments, and factors needed to compare the forecast accuracy of pooling methods. Last, we present basic principles for applying pooling methods and supporting empirical results. Conditions favoring pooling include time series with high volatility and outliers. Simple pooling methods are more accurate than complex methods, and we recommend manual intervention for cases with few time series.

Economic, organizational, and political influences on biases in forecasting state sales tax receipts

Abstract This paper investigates factors influencing fixed bias in forecasting state sales taxes revenues. By extending an existing model used to explain forecast accuracy to include a series of complex interactions related to the potential political and policy use of revenue forecasts, the paper extends our understanding of the forecasting process in government. Exploratory empirical analysis based on survey data is used to provide evidence that bias in forecasting results, at least in part, from political and policy manipulation. There is also evidence that institutional reforms associated with ‘good management’ practices affect forecast bias, but in complex ways depending upon the extent to which political competition exists within the state.

Predicting criminal recidivism: A comparison of neural network models with statistical methods

This article applies neural network and conventional statistical models to predicting criminal recidivism. While having promising properties for predicting recidivism, the network models do not exhibit any advantage over the other methods in an application on a well-known data set. Analysis suggests that currently available prediction variables have limited information content for discriminating recidivists, regardless of the models or methods used.

Location of Service Facilities for the Elderly

Senior centers provide a variety of supportive services for independent elderly adults. In many metropolitan areas, the elderly population is growing and redistributing from central cities to suburbs, where accessibility to senior centers is limited. Policy analysts need to locate senior centers to best meet changing demands for service. We present alternative hierarchical facility location models for senior centers applied to Allegheny County, Pennsylvania. We find that a model that minimizes consumer disutility and unserved demands is preferred to one that maximizes utility alone, and that the former model is well-behaved in response to changes in structural parameters.

Spatial decision support system for home-delivered services

Abstract. We present a spatial decision support system for the non-profit sector, designed to assist planning in the area of home-delivered services such as meals on wheels. Using data collected from existing programs, current and forecasted demographic data, and a set of algorithmic tools, we provide a system for evaluating current meals on wheels facilities, and for making incremental facility location decisions that satisfy coverage and equity requirements.

Organizational pressures on forecast evaluation: Managerial, political, and procedural influences

This paper proposes a theory to explain why some forecasting organizations institutionalize forecast accuracy evaluation while others do not. The theory considers internal and external aspects of managerial, political, and procedural factors as they affect forecasting organizations. The theory is then tested using data from a survey of the US Federal Forecasters Group. Though some support for the theory is developed, multiple alternative explanations for results and the ‘public’ nature of the sample organizations prevent wide-scale generalization. The results suggest that larger organizations are more likely to have some form of forecast evaluation than smaller units. The institutionalization of forecast accuracy evaluation is closely linked to internal managerial and procedural factors, while external political pressure tends to reduce the likelihood of institutionalization of evaluation of forecast accuracy.© 1997 John Wiley & Sons, Ltd.