Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model

The truncated Poisson regression model is used to arrive at point and interval estimates of the size of two offender populations, i.e. drunk drivers and persons who illegally possess firearms. The dependent capture–recapture variables are constructed from Dutch police records and are counts of individual arrests for both violations. The population size estimates are derived assuming that each count is a realization of a Poisson distribution, and that the Poisson parameters are related to covariates through the truncated Poisson regression model. These assumptions are discussed in detail, and the tenability of the second assumption is assessed by evaluating the marginal residuals and performing tests on overdispersion. For the firearms example, the second assumption seems to hold well, but for the drunk drivers example there is some overdispersion. It is concluded that the method is useful, provided it is used with care.     

A genetic algorithm for the partial binary constraint satisfaction problem: an application to a frequency assignment problem

We describe a genetic algorithm for the partial constraint satisfaction problem. The typical elements of a genetic algorithm, selection, mutation and cross-over, are filled in with combinatorial ideas. For instance, cross-over of two solutions is performed by taking the one or two domain elements in the solutions of each of the variables as the complete domain of the variable. Then a branch-and-bound method is used for solving this small instance. When tested on a class of frequency assignment problems this genetic algorithm produced the best known solutions for all test problems. This feeds the idea that combinatorial ideas may well be useful in genetic algorithms.     

Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models

Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in forecasting performance from exploiting these estimates using a real‐time dataset on UK aggregate expenditure data. Copyright © 2009 John Wiley & Sons, Ltd.     

Latent variable models for time series: A frequency domain approach with an application to the permanent income hypothesis

The theory of estimation and inference in a very general class of latent variable models for time series is developed by showing that the distribution theory for the finite Fourier transform of the observable variables in latent variable models for time series is isomorphic to that for the observable variables themselves in classical latent variable models. This implies that analytic work on classical latent variable models can be adapted to latent variable models for time series, an implication which is illustrated here in the context of a general canonical form. To provide an empirical example a latent variable model for permanent income is developed, its parameters are shown to be identified, and a variety of restrictions on these parameters implied by the permanent income hypothesis are tested.     

Multidimensional quality-of-life measure with an application to Fortune's best cities

The notion of the quality of life is about a finite set of measurable attributes that can be weighted by some metric. The quality of life has subjective as well as objective dimensions. Single dimension measures now are recognized as too narrow to fully capture differences in the quality of life. Based upon data envelopment analysis (DEA), the current paper demonstrates how to develop a multidimensional measure to characterize the quality of life and identify its best-practice frontier which balances work and family life and judges practical comfort. Benchmarks are introduced into DEA models to implicitly reflect tradeoff information on quality-of-life related factors and to incorporate evaluation standards. A method is proposed to determine the unique best quality-of-life scale target. Critical quality-of-life factors are identified in a multidimensional construct. Fortune magazine's choice of the 20 best cities—15 domestic and five international—is investigated.     

Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality

This paper extends the method of local instrumental variables developed by Heckman and Vytlacil [Heckman, J., Vytlacil E., 2005. Structural equations, treatment, effects and econometric policy evaluation. Econometrica 73(3), 669–738] to the estimation of not only means, but also distributions of potential outcomes. The newly developed method is illustrated by applying it to changes in college enrollment and wage inequality using data from the National Longitudinal Survey of Youth of 1979. Increases in college enrollment cause changes in the distribution of ability among college and high school graduates. This paper estimates a semiparametric selection model of schooling and wages to show that, for fixed skill prices, a 14% increase in college participation (analogous to the increase observed in the 1980s), reduces the college premium by 12% and increases the 90–10 percentile ratio among college graduates by 2%.     

The benefits of non-radial vs. radial super-efficiency DEA: an application to burden-sharing amongst NATO member nations

Using radial super-efficiency data envelopment analysis (DEA) has improved the discriminating performance across efficient decision-making units (DMUs). This paper extends the super-efficiency approach to a non-radial super-efficiency DEA (NRSE-DEA) index. NRSE-DEA is shown to be invariant to units of input (output) measurement. NRSE-DEA is illustrated here via an application to NATO burden-sharing assessment in which the DMUs are the member nations of NATO. The NRSE-DEA provides additional insights into the ranking of efficient countries, suggesting which are absorbing a particularly large share of NATO responsibilities. The NRSE-DEA generates a smaller set of efficient DMUs. This, in turn, provides more discriminatory power, a more accurate measure of super-efficiency, a more meaningful ranking of the efficient burden sharing countries, and a more reliable assessment of contributions by NATO members, amongst other policy issues.     

A comparison of two model averaging techniques with an application to growth empirics

Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.     

Pseudoconvexity on a closed convex set: an application to a wide class of generalized fractional functions

The issue of the pseudoconvexity of a function on a closed set is addressed. It is proved that if a function has no critical points on the boundary of a convex set, then the pseudoconvexity on the interior guarantees the pseudoconvexity on the closure of the set. This result holds even when the boundary of the set contains line segments, and it is used to characterize the pseudoconvexity, on the nonnegative orthant, of a wide class of generalized fractional functions, namely the sum between a linear one and a ratio which has an affine function as numerator and, as denominator, the p-th power of an affine function. The relationship between quasiconvexity and pseudoconvexity is also investigated.     

The quantilogram: With an application to evaluating directional predictability

We propose a new diagnostic tool for time series called the quantilogram. The tool can be used formally and we provide the inference tools to do this under general conditions, and it can also be used as a simple graphical device. We apply our method to measure directional predictability and to test the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence interval or on comparing the cumulated squared autocorrelations with the corresponding critical value. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to S&P500 stock index return data. The empirical results suggest some directional predictability in returns. The evidence is strongest in mid range quantiles like 5–10% and for daily data. The evidence for predictability at the median is of comparable strength to the evidence around the mean, and is strongest at the daily frequency.     

Integrating decomposition approaches for the analysis of temporal changes in economic structure: an application to Chicago's economy from 1980 to 2000

This paper presents an attempt to integrate two flow decomposition methods to analyse temporal changes in a region's economic structure. The two methods of structural analysis are push–pull decomposition analysis and structural Q-analysis. Push–pull analysis presents a quasi-optimization decomposition of a set of matrices with actual intersectoral economic flows into a weighted set of matrices, while structural Q-analysis provides a form in which the structure of these decomposed flows can be considered. The paper provides an expository application to Chicago's economic structure over the period of 1980 to 2000, to reveal a complementary perspective of hollowing-out the production process in the Chicago economy that was identified in previous studies.     

Forecasting of density functions with an application to cross-sectional and intraday returns

This paper is concerned with the forecasting of probability density functions. Density functions are nonnegative and have a constrained integral, and thus do not constitute a vector space. The implementation of established functional time series forecasting methods for such nonlinear data is therefore problematic. Two new methods are developed and compared to two existing methods. The comparison is based on the densities derived from cross-sectional and intraday returns. For such data, one of our new approaches is shown to dominate the existing methods, while the other is comparable to one of the existing approaches.     

Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China

With growing investments in the state key laboratories (SKLs) of China, it is essential to assess the efficiency of the administrative departments regarding managing the SKLs of China. However, few studies have been conducted about the evaluation of the performance of Chinese administrative departments in managing SKLs. To fill this gap, this paper investigates the performance within 22 SKL administrative departments in China, based on a data envelopment analysis cross-efficiency prospect aggregation approach incorporating the risk preference of decision maker. The empirical results show the major findings: (a) serious imbalance exists regarding the investment of 22 SKL administrative departments; (b) there are great differences among the performance of 22 SKL administrative departments; (c) some SKL administrative departments face an insufficient situation regarding the outputs; (d) different risk attitudes of decision maker have the significant impact on the evaluation results of the 22 SKL administrative departments. Based on these findings, we provide several policy suggestions for the development of SKLs in China.     

The emperor's new clothes: a critique of the multivariate t regression model

Zellner (1976) proposed a regression model in which the data vector (or the error vector) is represented as a realization from the multivariate Student t distribution. This model has attracted considerable attention because it seems to broaden the usual Gaussian assumption to allow for heavier-tailed error distributions. A number of results in the literature indicate that the standard inference procedures for the Gaussian model remain appropriate under the broader distributional assumption, leading to claims of robustness of the standard methods. We show that, although mathematically the two models are different, for purposes of statistical inference they are indistinguishable. The empirical implications of the multivariate t model are precisely the same as those of the Gaussian model. Hence the suggestion of a broader distributional representation of the data is spurious, and the claims of robustness are misleading. These conclusions are reached from both frequentist and Bayesian perspectives.     

Forecasting using a periodic transfer function: with an application to the UK price of ferrous scrap

The familiar concept of cointegration enables us to determine whether or not there is a long-run relationship between two integrated time series. However, this may not capture short-run effects such as seasonality. Two series which display different seasonal effects can still be cointegrated. Seasonality may arise independently of the long-run relationship between two time series or, indeed, the long-run relationship may itself be seasonal. The market for recycled ferrous scrap displays these features: the US and UK scrap prices are cointegrated, yet the local markets exhibit different forms of seasonality. The paper addresses the problem of using both cointegrating and seasonal relationships in forecasting time series through the use of periodic transfer function models. We consider the problems of testing for cointegration between series with differing seasonal patterns and develop a periodic transfer function model for the US and UK scrap markets. Forecast comparisons with other time series models suggest that forecasting efficiency may be improved by allowing for periodicity but that such improvement is by no means guaranteed. The correct specification of the periodic component of the model is critical for forecast accuracy.     

Some cumulative logit models for panel-data analysis: an application to a dynamic analysis of personal efficacy

This paper introduces novel cumulative logit models for the panel-data analysis of transitions among ordered states of a polytomous dependent variable. The models differ from conventional cumulative logit models in that they can distinguish between covariate effects on the odds of having an upward transition and covariate effects on the odds of having a downward transition in the ordered states of the dependent variable. The new models are applied to panel data on personal efficacy and are used to identify asymmetric patterns in the effects of divorce and unemployment on changes in the level of personal efficacy. The effects of the two events on increasing the odds of having a downward transition are shown to be greater than their effects on decreasing the odds of having an upward transition. Some distinct characteristics of the effects of each event are also reported.     

Methodological Advances in DEA: A survey and an application for the Dutch electricity sector

We survey the methodological advances in DEA over the last 25 years and discuss the necessary conditions for a sound empirical application. We hope this survey will contribute to the further dissemination of DEA, the knowledge of its relative strengths and weaknesses, and the tools currently available for exploiting its full potential. Our main points are illustrated by the case of the DEA study used by the regulatory office of the Dutch electricity sector (Dienst Toezicht Elektriciteitswet; Dte) for setting price caps.     

A methodology to rank enterprise application integration platforms from a performance perspective: an analytic hierarchy process-based approach

ABSTRACT

Companies’ software ecosystems comprise applications that support their business processes. Frequently, these applications have been developed with different technologies and with no concern about integration. Integration platforms are tools that facilitate the development and execution of integration solutions. In this article, we propose a methodology to support software engineers in the decision-making process for an integration platform when performance is a central requirement. The proposed methodology adds objective criteria for performance assessment purposes and has been used to rank the five most popular integration platforms in order to prove its feasibility.     

Using economic impact analysis for arts management: an empirical application to a music institute in the USA

Economic impact analysis can be used strategically by arts managers to provide information pertaining to making and justifying effective managerial and marketing decisions. The three aspects of economic impact analysis—direct, indirect and induced—reveal information such as arts participants' expenditure patterns, rationale for expenditure, relationships between participant demographics and expenditure, and amount of expenditure outside arts institutions. Arts managers can use the information and data to match product offerings of the institution with those of its participants; increase the participants' participation in, satisfaction with and loyalty to the institution; increase the stature and validity of the arts institution within the community; price the products of the arts institute; and increase community and financial support for arts institutions. This synergistic use of economic impact analysis information with marketing information is illustrated using a music institute in the USA. Copyright © 2003 Henry Stewart Publications     

Entrepreneurial intent in a transitional economy: an application of the theory of planned behavior to Romanian students

This study examines entrepreneurial intent in Romanian students using the Theory of Planned Behavior (Ajzen, Adv. Exp. Soc. Psychol., 20:1–63, 1987). Using multiple regression analyses to test for mediation, we analyzed survey data from 324 Romanian students. Some of our findings were consistent with the Theory of Planned Behavior, while others were not. As expected, we found that self-efficacy and the desirabilities associated with creating a venture were positively related to entrepreneurial intent. However, surprisingly, we found that the more supportive the students’ referents were, the less likely the students were to have intentions of starting a business.