Spatial and Temporal Patterns of Commercial Burglaries

A bstract . The factors that are crucial in predicting burglary of commercial establishments are evaluated. Burglars are rational in their choice of target, in that they consider both the revenue generated by the burglary and the chances of being apprehended. Location of the target plays a major role; establishments located within three blocks of heavily travelled thoroughfares are less vulnerable to burglary than those located further away. The wealthier the community, the higher the probability of burglary of its commercial establishments. Retail establishments and businesses which are located in office parks are most vulnerable to burglary. Establishments which have been in business less than one year are more likely to be burgled; the longer a store is in business, the less likely it is to become a victim of burglary. Burglar alarms are the most effective deterrent available to commercial establishments, followed by the installation of exterior and interior lights. In general, the probability of burglary of non-alarmed properties is 4.57 times higher than of similar alarmed property. The study is based upon a detailed survey of commercial establishments in three suburban communities of Philadelphia. These communities vary in their locational, physical and socio-economic characteristics and represent many suburban localities throughout the United States.     

Forecasting residential burglary

Following the work of Dhiri et al. [Modelling and predicting property crime trends. Home Office Research Study 198 (1999). London: HMSO] at the Home Office predicting recorded burglary and theft for England and Wales to the year 2001, econometric and time series models were constructed for predicting recorded residential burglary to the same date. A comparison between the Home Office econometric predictions and the less alarming econometric predictions made in this paper identified the differences as stemming from the particular set of variables used in the models. However, the Home Office and one of our econometric models adopted an error correction form which appeared to be the main reason why these models predicted increases in burglary. To identify the role of error correction in these models, time series models were built for the purpose of comparison, all of which predicted substantially lower numbers of residential burglaries. The years 1998–2001 appeared to offer an opportunity to test the utility of error correction models in the analysis of criminal behaviour. Subsequent to the forecasting exercise carried out in 1999, recorded outcomes have materialised, which point to the superiority of time series models compared to error correction models for the short-run forecasting of property crime. This result calls into question the concept of a long-run equilibrium relationship for crime.     

What is the effect of university education on chances to be self-employed in transitional countries?: Instrumental variable analysis of cross-sectional sample of 29 nations

We used a high-quality cross-sectional data set that covers a diverse set of 29 transitional countries, to find the effect of education of probability of people being self-employed using standard probit models and instrumental variable biprobit that address endogeneity. Our findings suggest a negative effect of university education on the propensity of being self-employed. This finding remains the same for the single-stage model (i.e. standard probit) and the instrumental variable model (i.e. biprobit). We found strong endogeneity in the estimation of education effect on the propensity of being self-employed, ignoring which renders estimations biased. Regression models, which do not address endogeneity tend to underestimate the negative effect of the education on the probability of being self-employed in the countries of transition. Researchers should use alternative approaches to reduce endogeneity, such as instrumental variables and longitudinal analysis.     

The Distribution of Criminal Offenses in an Urban Environment: A Spatial Analysis of Criminal Spillovers and of Juvenile Offenders

A bstract . In order to derive a basis for an optimal allocation of police manpower among contiguous communities criminal spillovers among these communities must be explicitly taken into account. Under the assumptions established in the economic theory of choice a criminal spillovers model is derived. The hypothesized variables within this model which serve as motivating factors for criminal spillovers are economic gain and competitive criminal pressures. Those factors which deter criminal spillovers are costs of committing offenses and the relative risks in committing crimes in distant communities. The model is then tested using data on juvenile offenders supplied by the City of Los Angeles , for the crimes of robbery, burglary and larceny. The results suggest that on an aggregate basis juvenile criminal behavior is not purely opportunistic and that certain control and motivating factors are significant in the juvenile's judgments for the spatial selection of offense targets.     

Transient Crowding and Crime:

A bstract . The effects on crime rates of the intrusion of large numbers of strangers into an area are investigated. The sensitivity of six different crime types ( murder, rape, assault, robbery, burglary , and larceny ) to density of strangers is estimated using a standard model of criminal behavior. The variables which reflect this density are measures of tourists, college students, shoppers from other areas, opportunities to consume alcohol , and the presence of interstate highway exits. The results suggest that an increase in the number of strangers into an area has a positive effect on the crimes of burglary, larceny, and robbery, but very little effect on assault, murder and rape. A significant finding is that "wet" counties have a higher incidence of each crime type than do "dry" counties. From a policy standpoint, officials who support economic growth in urban areas should keep in mind that an increase in the opportunity for illegal activity accompanies such growth and plan accordingly.     

The market for street crime: An economic analysis of victim-offender interaction

This paper views the economic explanation of crime rates from a new perspective by explicitly deriving an economic model of the behavior of potential victims and considering the interaction of the behavior of potential victims with the behavior of offenders. Crime is produced through offenders supplying time in search of victims and victims demanding to expose themselves to the environment. A model is set up in which the crime rate, the probability of being a victim per unit of exposure time, and the probability of finding a victim per unit of offenders' search time are simultaneously determined. The model leads us to distinguish the “real” crime rate (the probability of being a victim per unit of exposure time) from the “nominal” crime rate (the number of crimes per capita). The nominal crime rate can be inversely related to the real crime rate.     

Predicting default risk under asymmetric binary link functions

In this article we propose the use of an asymmetric binary link function to extend the proportional hazard model for predicting loan default. The rationale behind this approach is that the symmetry assumption that has been widely used in the literature could be considered as quite restrictive, especially during periods of financial distress. In our approach we allow for a flexible level of asymmetry in the probability of default by the use of the skewed logit distribution. This enable us to estimate the actual level of asymmetry that is associated with the data at hand. We implement our approach to both simulated data and a rich micro dataset of consumer loan accounts. Our results provide clear evidence that ignoring the actual level of asymmetry leads to seriously biased estimates of the slope coefficients, inaccurate marginal effects of the covariates of the model, and overestimation of the probability of default. Regarding the predictive power of the covariates of the model, we have found that loan-specific covariates contain considerably more information about the loan default than macroeconomic covariates, which are often used in practice to carry out macroprudential stress testing.     

Pricing training and development programs using stochastic CVP analysis

This paper sets forth, analyzes and applies a stochastic cost‐volume‐profit (CVP) model specifically geared toward the determination of enrollment fees for training and development (T+D) programs. It is a simpler model than many of those developed in the research literature, but it does incorporate one advanced component: an ‘economic’ demand function relating the expected sales level to price. Price is neither a constant nor a random variable in this model but rather the decision‐maker's basic control variable. The simplicity of the model permits analytical solutions for five ‘special prices’: (1) the highest price which sets breakeven probability equal to a minimum acceptable level; (2) the price which maximizes expected profits; (3) the price which maximizes a Cobb–Douglas utility function based on expected profits and breakeven probability; (4) the price which maximizes breakeven probability; and (5) the lowest price which sets breakeven probability equal to a minimum acceptable level. The model is applied to data provided by the Center for Management and Professional Development at the authors' university. The results suggest that there could be a significant payoff to fine‐tuning a T+D provider's pricing strategy using formal analysis. Copyright © 2005 John Wiley & Sons, Ltd.     

A sequential model for cracking down on street markets for illicit drugs

This paper develops a sequential decision-making model for assisting law enforcement officials in allocating resources during a crackdown operation on illicit drug markets. The sequential crackdown model (SCM) considers a probabilistic framework, where the probability of incarceration of a dealer and the probability of dealing are modeled as a function of the size of a drug market, crackdown enforcement level, drug dealer's financial hardship, and other market characteristics.The model was developed and tested in consultation with enforcement officials from Philadelphia, PA and Camden, NJ. We present a detailed, step-by-step implementation scheme for updating parameters on each day of the crackdown. Parameter estimation along with examples of model usage is provided. Through these examples, we illustrate how the SCM could be helpful in understanding the response of illicit drug markets to various enforcement strategies. We further show conditions under which an alternating crackdown policy (referred to as a crackdown-backoff) or a consistent use of maximum possible enforcement would be optimal strategies for managing a drug crackdown operation. Within the context of the model and parameter estimates, we show that a much quicker and less costly collapse could be implemented if the available enforcement resources are increased. Finally, the model provides possible conditions under which a crackdown operation would be unsuccessful in eliminating a drug market.     

Crime and Unemployment Among Youths in the United States, 1958-1990

A bstract . Unemployment and arrest time series data for persons aged 16 to 19 years in the United States from 1958 to 1990 show crime among youth to be associated with both the current level of youth unemployment and the annual change in the rate of youth unemployment. For violent offenses (homicide, rape and aggravated assault ), as well as for property offenses (robbery, burglary and larceny ), higher rates of youth unemployment are negatively associated with annual changes in the arrest rates of youth. Conversely, the lagged effect of youth unemployment is limited to property offenses, where annual changes in youth unemployment are positively related to annual changes in the arrest rates of youth for robbery, burglary and larceny, but negatively related to changes in the auto theft arrest rate.     

The Misspecification of Arma Models

The object of this paper is to assess the effects of fitting a model of the wrong order to a time series which is generated by an autoregressive moving–average process. The method is to examine the spectral density functions which are indicated by the probability limits of the least–squares estimators of the misspecified models. The least–squares estimates are asymptotically equivalent to the maximum–likelihood estimates.
The experiments reported in this paper suggest that, if the spectral density function of the data–generating process displays prominent modes, then there is a danger of being seriously misled about the nature of the process whenever one fits a model with too few parameters. It also transpires that, in such cases, the criterion function is liable to have several local minima.
Autoregressive moving–average models are being used increasingly in object detection applications where the spectrum of a signal serves to identify the nature of its source. Our results suggest that radical misidentifications can result from the use of incorrectly parametrised models.     

A tale of two correlations: Evidence and theory regarding the phase shift between the price level and output

We examine the relationship between the price level and output at business-cycle frequencies. In the postwar period, there is evidence of a phase shift between the price level and output. Such a phase shift is manifested in the price level being countercyclical and the inflation rate being procyclical or acyclical, depending on the detrending method used. Our examination takes three approaches. First, we apply bootstrapping methods to characterize the two correlations, though the methodology could easily be extended to any set of facts. Second, we specify a model economy with forecast heterogeneity, showing numerically that this model economy can match the observed pair of correlations. Third, we apply robust control theory, deriving conditions in which the price level is countercyclical and the inflation rate is procyclical.     

Patents, Invalidity, and the Strategic Transmission of Enabling Information

The patent system encourages innovation and knowledge disclosure by providing exclusivity to inventors. Exclusivity is limited, however, because a substantial fraction of patents have some probability of being ruled invalid when challenged in court. The possibility of invalidity—and an ensuing market competition—suggests that when an innovator's capability (e.g., cost of production) is private information, there is potential value to an innovator from signaling strong capability via a disclosure that transfers technical knowledge to a competitor. We model a product-innovation setting in which a valid patent gives market exclusivity and find a unique signaling equilibrium. One might expect that as the probability that a patent will be invalid becomes low, greater disclosure will be induced. We do not find this expectation to be generally supported. Further, even where full disclosure arises in equilibrium, it is only the less capable who make full disclosures. The equilibrium analysis also highlights many of the novel and appealing features of enabling knowledge disclosure signals.     

Adapting football to the child: an application of the logistic regression model in observational methodology

Logistic regression is included into the analysis techniques which are valid for observational methodology. However, its presence at the heart of this methodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child’s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played.     

A Logit Model with Missing Information Illustrated by Testing for Hidden Unemployment in Transition Economies

In an important paper, Dempster, Laird and Rubin (1977) showed how the expectation maximization (EM) algorithm could be used to obtain maximum likelihood estimates of parameters in a multinomial probability model with missing information. This article extends Dempster, Laird and Rubin's work on the EM algorithm to the estimation of a multinomial logit model with missing information on category membership. We call this new model the latent multinomial logit (LMNL) model. A constrained version of the LMNL model is used to examine the issue of hidden unemployment in transition economies following the approach of Earle and Sakova (2000) . We found an additional 0.5% hidden unemployment among workers describing themselves as self‐employed in the transition economies of Central and Eastern Europe.     

网络计划多目标优化问题的研究

对网络计划多目标优化的问题进行了探讨，在工期一费用优化分析的基础上，建立了工期、费用、工程质量、完工概率及资源均衡等多目标综合优化的模型，试图在整体上解决网络计划的优化问题。     

Model occurrence and model selection in panel data sets

In this study we focus attention on model selection in the presence of panel data. Our approach is eclectic in that it combines both classical and Bayesian techniques. It is also novel in that we address not only model selection, but also model occurrence, i.e., the process by which ‘nature’ chooses a statistical framework in which to generate the data of interest. For a given data subset, there exist competing models each of which have an ex ante positive probability of being the correct model, but for any one generated sample, ex post exactly one such model is the basis for the observed data set. Attention focuses on how the underlying model occurrence probabilities of the competing models depend on characteristics of the environments in which the data subsets are generated. Classical, Bayesian, and mixed estimation approaches are developed. Bayesian approaches are shown to be especially attractive whenever the models are nested.     

The competing risks of acquiring and being acquired: Evidence from Colombia's financial sector

This paper studies the determinants of the probability of participating in a process of merging or acquisition for financial institutions in Colombia. We use survival analysis techniques and competing risks models to estimate the probability of participating in such processes as an acquiring or acquired firm. Using an especially rich database containing financial information of Colombian banks for the period 1990–2007, we find that both macroeconomic and microeconomic variables are important determinants of this probability. However, there are differential effects for the acquiring firm and the acquired firm. Particularly, while firm size and solvency are significant determinants of the probability of being an acquiring firm, efficiency is an important determinant of the probability of being acquired. Also, the concentration index, which plays no role for acquiring firms, plays an important role in the probability of being acquired.     

The Probability That a Smoker Does Not Purchase Tobacco: A Note

In this note we are concerned with the estimation of the probability that a smoker does not purchase tobacco during a survey. Usually, tobacco demand has been estimated using limited dependent variable models under the assumption that an important proportion of smokers declared a zero expenditure in tobacco. However, if the probability of non purchasing by a smoker is negligible, zeros could be ignored and the demand equation could be estimated on positive expenditure data using traditional estimation methods. Here we estimate this probability and find that it is extremely small. A novelty of this work is the use of data on the quantity and frequency of tobacco purchases during the week of the survey, instead of the more commonly used expenditure data.     

Combining multiple probability predictions using a simple logit model

This paper begins by presenting a simple model of the way in which experts estimate probabilities. The model is then used to construct a likelihood-based aggregation formula for combining multiple probability forecasts. The resulting aggregator has a simple analytical form that depends on a single, easily-interpretable parameter. This makes it computationally simple, attractive for further development, and robust against overfitting. Based on a large-scale dataset in which over 1300 experts tried to predict 69 geopolitical events, our aggregator is found to be superior to several widely-used aggregation algorithms.