bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation in Multitype Epidemics
Authors: Britton Tom
Published: March 2003

A multitype epidemic model is analysed assuming proportionate mixing between types. Estimation procedures for the susceptibilities and infectivities are derived for three sets of data: complete data, meaning that the whole epidemic process is observed continuously; the removal processes are observed continuously; only the final state is observed. Under the assumption of a major outbreak in a population of size n it is shown that, for all three data sets, the susceptibility estimators are always efficient, i.e. consistent with a √n rate of convergence. The infectivity estimators are ‘in most cases’ respectively efficient, efficient and unidentifiable. However, if some susceptibilities are equal then the corresponding infectivity estimators are respectively barely consistent (√log(n) rate of convergence), not consistent and unidentifiable. The estimators are applied to simulated data.
Empirical Transform Estimation for Indexed Stochastic Models
Authors: Yao Qiwei , Morgan Byron J. T.
Published: March 2003

We present a method for estimating the parameters in indexed stochastic models via a least squares approach based on empirical transforms. Asymptotic approximations are derived for the distribution of the resulting estimators. An explicit expression for the mean-squared error provides a natural way of selecting the transform variable, and a numerical example illustrates the performance of the resulting method. A common finding, which we term ‘diagonal optimization’, occurs when multiparameter models are fitted by using transforms. Diagonal optimization arises when optimal performance results from equating the elements of the transform vector, and we provide a heuristic explanation of why this occurs.
Multivariate Boundary Kernels and a Continuous Least Squares Principle
Authors: Müller H. G. , Stadtmüller U.
Published: March 2003

Whereas there are many references on univariate boundary kernels, the construction of boundary kernels for multivariate density and curve estimation has not been investigated in detail. The use of multivariate boundary kernels ensures global consistency of multivariate kernel estimates as measured by the integrated mean-squared error or sup-norm deviation for functions with compact support. We develop a class of boundary kernels which work for any support, regardless of the complexity of its boundary. Our construction yields a boundary kernel for each point in the boundary region where the function is to be estimated. These boundary kernels provide a natural continuation of non-negative kernels used in the interior onto the boundary. They are obtained as solutions of the same kernel-generating variational problem which also produces the kernel function used in the interior as its solution. We discuss the numerical implementation of the proposed boundary kernels and their relationship to locally weighted least squares. Along the way we establish a continuous least squares principle and a continuous analogue of the Gauss–Markov theorem.
Latent Variable Models with Mixed Continuous and Polytomous Data
Authors: Shi Jian-Qing , Lee Sik-Yum
Published: March 2003

Owing to the nature of the problems and the design of questionnaires, discrete polytomous data are very common in behavioural, medical and social research. Analysing the relationships between the manifest and the latent variables based on mixed polytomous and continuous data has proven to be difficult. A general structural equation model is investigated for these mixed outcomes. Maximum likelihood (ML) estimates of the unknown thresholds and the structural parameters in the covariance structure are obtained. A Monte Carlo–EM algorithm is implemented to produce the ML estimates. It is shown that closed form solutions can be obtained for the M-step, and estimates of the latent variables are produced as a by-product of the analysis. The method is illustrated with a real example.
Local Likelihood Smoothing of Sample Extremes
Authors: Davison A. C. , Ramesh N. I.
Published: March 2003

Trends in sample extremes are of interest in many contexts, an example being environmental statistics. Parametric models are often used to model trends in such data, but they may not be suitable for exploratory data analysis. This paper outlines a semiparametric approach to smoothing sample extremes, based on local polynomial fitting of the generalized extreme value distribution and related models. The uncertainty of fits is assessed by using resampling methods. The methods are applied to data on extreme temperatures and on record times for the women's 3000 m race.
Regression Analysis of Panel Count Data with Covariate-Dependent Observation and Censoring Times
Authors: Sun Jianguo , Wei L. J.
Published: March 2003

Panel count data often occur in a long-term study where the primary end point is the time to a specific event and each subject may experience multiple recurrences of this event. Furthermore, suppose that it is not feasible to keep subjects under observation continuously and the numbers of recurrences for each subject are only recorded at several distinct time points over the study period. Moreover, the set of observation times may vary from subject to subject. In this paper, regression methods, which are derived under simple semiparametric models, are proposed for the analysis of such longitudinal count data. Especially, we consider the situation when both observation and censoring times may depend on covariates. The new procedures are illustrated with data from a well-known cancer study.
Blur-generated non-separable space–time models
Authors: Brown Patrick E. , Roberts Gareth O. , Kåresen Kjetil F. , Tonellato Stefano
Published: March 2003

Statistical space–time modelling has traditionally been concerned with separable covariance functions, meaning that the covariance function is a product of a purely temporal function and a purely spatial function. We draw attention to a physical dispersion model which could model phenomena such as the spread of an air pollutant. We show that this model has a non-separable covariance function. The model is well suited to a wide range of realistic problems which will be poorly fitted by separable models. The model operates successively in time: the spatial field at time t +1 is obtained by ‘blurring’ the field at time t and adding a spatial random field. The model is first introduced at discrete time steps, and the limit is taken as the length of the time steps goes to 0. This gives a consistent continuous model with parameters that are interpretable in continuous space and independent of sampling intervals. Under certain conditions the blurring must be a Gaussian smoothing kernel. We also show that the model is generated by a stochastic differential equation which has been studied by several researchers previously.
Bayesian Regression with Multivariate Linear Splines
Authors: Holmes C. C. , Mallick B. K.
Published: March 2003

We present a Bayesian analysis of a piecewise linear model constructed by using basis functions which generalizes the univariate linear spline to higher dimensions. Prior distributions are adopted on both the number and the locations of the splines, which leads to a model averaging approach to prediction with predictive distributions that take into account model uncertainty. Conditioning on the data produces a Bayes local linear model with distributions on both predictions and local linear parameters. The method is spatially adaptive and covariate selection is achieved by using splines of lower dimension than the data.
Moment Estimation for Statistics from Marked Point Processes
Authors: Politis Dimitris N. , Sherman Michael
Published: March 2003

In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L  2-consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated parameters. A practical data-based method for choosing a subsampling parameter is given and illustrated on a data set. Finite sample simulation examples are also presented.
Inference in High Dimensional Generalized Linear Models Based on Soft Thresholding
Authors: Klinger Artur
Published: March 2003

We further develop and analyse penalized likelihood estimators for generalized linear models with a large number of coefficients. The methodology proposed leads to an adaptive selection of model terms without substantial variance inflation. Our proposal extends the soft thresholding strategy of Donoho and Johnstone and the lasso of Tibshirani to generalized linear models and multiple predictor variables. In addition, we develop an estimator for the covariance matrix of the estimated coefficients, which can even be used for terms dropped from the model. Used in connection with basis functions, the methodology proposed provides an alternative to other generalized function estimators. It leads to an adaptive economical description of the results in terms of basis functions. Specifically, it is shown how adaptive regression splines and qualitative restrictions can be incorporated. Our approach is demonstrated by applications to a prognosis of solvency and rental guides.
Generalized Case–cohort Sampling
Authors: Chen Kani
Published: March 2003

A class of cohort sampling designs, including nested case–control, case–cohort and classical case–control designs involving survival data, is studied through a unified approach using Cox’s proportional hazards model. By finding an optimal sample reuse method via local averaging, a closed form estimating function is obtained, leading directly to the estimators of the regression parameters that are relatively easy to compute and are more efficient than some commonly used estimators in case–cohort and nested case–control studies. A semiparametric efficient estimator can also be found with some further computation. In addition, the class of sampling designs in this study provides a variety of sampling options and relaxes the restrictions of sampling schemes that are currently available.
High Order Data Sharpening for Density Estimation
Authors: Hall Peter , Minnotte Michael C.
Published: March 2003

It is shown that data sharpening can be used to produce density estimators that enjoy arbitrarily high orders of bias reduction. Practical advantages of this approach, relative to competing methods, are demonstrated. They include the sheer simplicity of the estimators, which makes code for computing them particularly easy to write, very good mean-squared error performance, reduced ‘wiggliness' of estimates and greater robustness against undersmoothing.
Chain Graph Models and their Causal Interpretations
Authors: Lauritzen Steffen L. , Richardson Thomas S.
Published: March 2003

Chain graphs are a natural generalization of directed acyclic graphs and undirected graphs. However, the apparent simplicity of chain graphs belies the subtlety of the conditional independence hypotheses that they represent. There are many simple and apparently plausible, but ultimately fallacious, interpretations of chain graphs that are often invoked, implicitly or explicitly. These interpretations also lead to flawed methods for applying background knowledge to model selection. We present a valid interpretation by showing how the distribution corresponding to a chain graph may be generated from the equilibrium distributions of dynamic models with feed-back. These dynamic interpretations lead to a simple theory of intervention, extending the theory developed for directed acyclic graphs. Finally, we contrast chain graph models under this interpretation with simultaneous equation models which have traditionally been used to model feed-back in econometrics.
Assessing Population Differentiation and Isolation from Single-Nucleotide Polymorphism Data
Authors: Nicholson George , Smith Albert V. , Jónsson Frosti , Gústafsson Ómar , Stefánsson Kári , Donnelly Peter
Published: March 2003

We introduce a new, hierarchical, model for single-nucleotide polymorphism allele frequencies in a structured population, which is naturally fitted via Markov chain Monte Carlo methods. There is one parameter for each population, closely analogous to a population-specific version of Wright's FST, which can be interpreted as measuring how isolated the relevant population has been. Our model includes the effects of single-nucleotide polymorphism ascertainment and is motivated by population genetics considerations, explicitly in the transient setting after divergence of populations, rather than as the equilibrium of a stochastic model, as is traditionally the case. For the sizes of data set that we consider the method provides good parameter estimates and considerably outperforms estimation methods analogous to those currently used in practice. We apply the method to one new and one existing human data set, each with rather different characteristics—the first consisting of three rather close European populations; the second of four populations taken from across the globe. A novelty of our framework is that the fit of the underlying model can be assessed easily, and these results are encouraging for both data sets analysed. Our analysis suggests that Iceland is more differentiated than the other two European populations (France and Utah), a finding which is consistent with the historical record, but not obvious from comparisons of simple summary statistics.
Posterior Bimodality in the Balanced One-Way Random-Effects Model
Authors: Liu Jiannong , Hodges James S.
Published: March 2003

Although some researchers have examined posterior multimodality for specific richly parameterized models, multimodality is not well characterized for any such model. The paper characterizes bimodality of the joint and marginal posteriors for a conjugate analysis of the balanced one-way random-effects model with a flat prior on the mean. This apparently simple model has surprisingly complex and even bizarre mode behaviour. Bimodality usually arises when the data indicate a much larger between-groups variance than does the prior. We examine an example in detail, present a graphical display for describing bimodality and use real data sets from a statistical practice to shed light on the practical relevance of bimodality for these models.
Estimating the Association Parameter for Copula Models Under Dependent Censoring
Authors: Wang Weijing
Published: March 2003

Many biomedical studies involve the analysis of multiple events. The dependence between the times to these end points is often of scientific interest. We investigate a situation when one end point is subject to censoring by the other. The model assumptions of Day and co-workers and Fine and co-workers are extended to more general structures where the level of association may vary with time. Two types of estimating function are proposed. Asymptotic properties of the proposed estimators are derived. Their finite sample performance is studied via simulations. The inference procedures are applied to two real data sets for illustration.
Non-linear Fourier Time Series Analysis for Human Brain Mapping by Functional Magnetic Resonance Imaging
Authors: Lange Nicholas , Zeger Scott L.
Published: March 2003

A non-linear parametric model for brain activation detection by functional magnetic resonance imaging (FMRI) is proposed. The effects of a designed temporal stimulus on the FMRI signal at each brain location in a 36×60 spatial grid are estimated from discrete Fourier transforms of the observed time series at each location. The frequency domain regression model accommodates unobservable and spatially varying haemodynamic response functions through their estimated convolutions with the global stimulus. This approach generalizes an existing method for human brain mapping. An experiment to detect focal cortical activation during primary visual stimulation demonstrates the usefulness of the method.
Discrete Scale Models for Survival–Sacrifice Experiments
Authors: Rai S. N. , Matthews D. E.
Published: March 2003

Analyses of carcinogenicity experiments involving occult (hidden) tumours are usually based on cause-of-death information or the results of many interim sacrifices. A simple compartmental model is described that does not involve the cause of death. The method of analysis requires only one interim sacrifice, in addition to the usual terminal kill, to ensure that the tumour incidence rates can be estimated. One advantage of the approach is demonstrated in the analysis of glomerulosclerosis following exposure to ionizing radiation. Although the semiparametric model involves fewer parameters, estimates of key functions derived in this analysis are similar to those obtained previously by using a nonparametric method that involves many more parameters.
Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus
Authors: Lipsitz Stuart R. , Fitzmaurice Garrett M. , Molenberghs Geert , Zhao Lue Ping
Published: March 2003

Patients infected with the human immunodeficiency virus (HIV) generally experience a decline in their CD4 cell count (a count of certain white blood cells). We describe the use of quantile regression methods to analyse longitudinal data on CD4 cell counts from 1300 patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. It is of scientific interest to determine any treatment differences in the CD4 cell counts over a short treatment period. However, the analysis of the CD4 data is complicated by drop-outs: patients with lower CD4 cell counts at the base-line appear more likely to drop out at later measurement occasions. Motivated by this example, we describe the use of ‘weighted’ estimating equations in quantile regression models for longitudinal data with drop-outs. In particular, the conventional estimating equations for the quantile regression parameters are weighted inversely proportionally to the probability of drop-out. This approach requires the process generating the missing data to be estimable but makes no assumptions about the distribution of the responses other than those imposed by the quantile regression model. This method yields consistent estimates of the quantile regression parameters provided that the model for drop-out has been correctly specified. The methodology proposed is applied to the CD4 cell count data and the results are compared with those obtained from an ‘unweighted’ analysis. These results demonstrate how an analysis that fails to account for drop-outs can mislead.
Fitting Markov Chain Models to Discrete State Series such as DNA Sequences
Authors: Avery Peter J. , Henderson Daniel A.
Published: March 2003

Discrete state series such as DNA sequences can often be modelled by Markov chains. The analysis of such series is discussed in the context of log-linear models. The data produce contingency tables with similar margins due to the dependence of the observations. However, despite the unusual structure of the tables, the analysis is equivalent to that for data from multinomial sampling. The reason why the standard number of degrees of freedom is correct is explained by using theoretical arguments and the asymptotic distribution of the deviance is verified empirically. Problems involved with fitting high order Markov chain models, such as reduced power and computational expense, are also discussed.
Multilevel Modelling of the Geographical Distributions of Diseases
Authors: Langford Ian H. , Leyland Alistair H. , Rasbash Jon , Goldstein Harvey
Published: March 2003

Multilevel modelling is used on problems arising from the analysis of spatially distributed health data. We use three applications to demonstrate the use of multilevel modelling in this area. The first concerns small area all-cause mortality rates from Glasgow where spatial autocorrelation between residuals is examined. The second analysis is of prostate cancer cases in Scottish counties where we use a range of models to examine whether the incidence is higher in more rural areas. The third develops a multiple-cause model in which deaths from cancer and cardiovascular disease in Glasgow are examined simultaneously in a spatial model. We discuss some of the issues surrounding the use of complex spatial models and the potential for future developments.
Simple Principal Components
Authors: Vines S. K.
Published: March 2003

We introduce an algorithm for producing simple approximate principal components directly from a variance–covariance matrix. At the heart of the algorithm is a series of ‘simplicity preserving’ linear transformations. Each transformation seeks a direction within a two-dimensional subspace that has maximum variance. However, the choice of directions is limited so that the direction can be represented by a vector of integers whenever the subspace can also be represented by vector if integers. The resulting approximate components can therefore always be represented by integers. Furthermore the elements of these integer vectors are often small, particularly for the first few components. We demonstrate the performance of this algorithm on two data sets and show that good approximations to the principal components that are also clearly simple and interpretable can result.
Sensitivity Analysis for Incomplete Contingency Tables: The Slovenian Plebiscite Case
Authors: Molenberghs Geert , Kenward Michael G. , Goetghebeur Els
Published: March 2003

Classical inferential procedures induce conclusions from a set of data to a population of interest, accounting for the imprecision resulting from the stochastic component of the model. Less attention is devoted to the uncertainty arising from (unplanned) incompleteness in the data. Through the choice of an identifiable model for non-ignorable non-response, one narrows the possible data-generating mechanisms to the point where inference only suffers from imprecision. Some proposals have been made for assessing the sensitivity to these modelling assumptions; many are based on fitting several plausible but competing models. For example, we could assume that the missing data are missing at random in one model, and then fit an additional model where non-random missingness is assumed. On the basis of data from a Slovenian plebiscite, conducted in 1991, to prepare for independence, it is shown that such an ad hoc procedure may be misleading. We propose an approach which identifies and incorporates both sources of uncertainty in inference: imprecision due to finite sampling and ignorance due to incompleteness. A simple sensitivity analysis considers a finite set of plausible models. We take this idea one step further by considering more degrees of freedom than the data support. This produces sets of estimates (regions of ignorance) and sets of confidence regions (combined into regions of uncertainty).
On the Effect of the Number of Quadrature Points in a Logistic Random Effects Model: An Example
Authors: Lesaffre Emmanuel , Spiessens Bart
Published: March 2003

Although generalized linear mixed models are recognized to be of major practical importance, it is also known that they can be computationally demanding. The problem is the evaluation of the integral in calculating the marginalized likelihood. The straightforward method is based on the Gauss–Hermite technique, based on Gaussian quadrature points. Another approach is provided by the class of penalized quasi-likelihood methods. It is commonly believed that the Gauss–Hermite method works relatively well in simple situations but fails in more complicated structures. However, we present here a strikingly simple example of a logistic random-intercepts model in the context of a longitudinal clinical trial where the method gives valid results only for a high number of quadrature points (Q). As a consequence, this result warns the practitioner to examine routinely the dependence of the results on Q. The adaptive Gaussian quadrature, as implemented in the new SAS procedure NLMIXED, offered the solution to our problem. However, even the adaptive version of Gaussian quadrature needs careful handling to ensure convergence.
Building Marginal Models for Multiple Ordinal Measurements
Authors: Huang Guan-Hua , Bandeen-Roche Karen , Rubin Gary S
Published: March 2003

Biomedical and psychosocial researchers increasingly utilize multiple indicators to assess an outcome of interest. We apply the ordinal estimating equations model for analysing this kind of measurement. We detail the special complexities of using this model to analyse clustered non-identical items and propose a workable model building strategy. Three graphical methods— cumulative log-odds, partial residual and Pearson residual plotting—are developed to diagnose the adequacy of models. The benefit of incorporating interitem associations and the trade-off between simple versus complex models are evaluated. Throughout the paper, an analysis to determine how measured impairments affect visual disability is used for illustration.
The Bayesian Choice of Crop Variety and Fertilizer Dose
Authors: Theobald Chris M , Talbot Mike
Published: March 2003

Recent contributions to the theory of optimizing fertilizer doses in agricultural crop production have introduced Bayesian ideas to incorporate information on crop yield from several environments and on soil nutrients from a soil test, but they have not used a fully Bayesian formulation. We present such a formulation and demonstrate how the resulting Bayes decision procedure can be evaluated in practice by using Markov chain Monte Carlo methods. The approach incorporates expert knowledge of the crop and of regional and local soil conditions and allows a choice of crop variety as well as of fertilizer level. Alternative dose–response functions are expressed in terms of a common interpretable set of parameters to facilitate model comparisons and the specification of prior distributions. The approach is illustrated with a set of yield data from spring barley nitrogen–response trials and is found to be robust to changes in the dose–response function and the prior distribution for indigenous soil nitrogen.
A Generalized Estimating Equation Approach to Modelling Incompatible Data Formats with Covariate Measurement Error: Application to Human Immunodeficiency Virus Immune Markers
Authors: Kowalski J , Tu X. M
Published: March 2003

The integration of technological advances into research studies often raises an issue of incompatibility of data. This problem is common to longitudinal and multicentre studies, taking the form of changes in the definitions, acquisition of data or measuring instruments of some study variables. In our case of studying the relationship between a marker of immune response to human immunodeficiency virus and human immunodeficiency virus infection status, using data from the Multi-Center AIDS Cohort Study, changes in the manufactured tests used for both variables occurred throughout the study, resulting in data with different manufactured scales. In addition, the latent nature of the immune response of interest necessitated a further consideration of a measurement error component. We address the general issue of incompatibility of data, together with the issue of covariate measurement error, in a unified, generalized linear model setting with inferences based on the generalized estimating equation framework. General conditions are constructed to ensure consistent estimates and their variances for the primary model of interest, with the asymptotic behaviour of resulting estimates examined under a variety of modelling scenarios. The approach is illustrated by modelling a repeated ordinal response with incompatible formats, as a function of a covariate with incompatible formats and measurement error, based on the Multi-Center AIDS Cohort Study data.
Inverse Gaussian Autoregressive Models
Authors: Abraham B. , Balakrishna N.
Published: March 2003

A first‐order autoregressive process with one‐dimensional inverse Gaussian marginals is introduced. The innovation distributions are obtained in certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal. Performance of the estimators is discussed using simulation experiments.
Testing Stochastic Cycles in Macroeconomic Time Series
Authors: Gil‐Alana L. A.
Published: March 2003

A particular version of the tests of Robinson (1994) for testing stochastic cycles in macroeconomic time series is proposed in this article. The tests have a standard limit distribution and are easy to implement in raw time series. A Monte Carlo experiment is conducted, studying the size and the power of the tests against different alternatives, and the results are compared with those based on other tests. An empirical application using historical US annual data is also carried out at the end of the article.
A note on calculating autocovariances of long‐memory processes
Authors: BERTELLI STEFANO , CAPORIN MASSIMILIANO
Published: March 2003

In this paper, we consider a method (splitting) for calculating the auto‐ covariances of fractional integrated processes (ARFIMA) and generalized integrated processes (GARMA). The splitting method does not require any restriction on the autoregressive roots, and allows fast calculation of the autocovariances of these processes.
Wage Flexibility for Skilled and Unskilled Workers: New Evidence on the Norwegian Wage Curve
Authors: Johansen Kåre
Published: March 2003

Empirical evidence is provided in favour of a hypothesis that wages for unskilled workers are more responsive to unemployment than wages for skilled workers. The results imply vigorous wage responsiveness to low levels of unemployment for both groups, while the wage curves become almost entirely flat for unemployment rates above 1.7 percent. One interpretation of this result is that firms have strong incentives to increase wages in order to recruit and retain workers when unemployment is below some critical level. Since unemployed workers will certainly find work, the expected costs of a job loss are small, as are costs associated with an egalitarian wage policy.
The Inadequacy of Mainstream Theories ofTrade Union Behaviour
Authors: Fleetwood Steve
Published: March 2003

This paper identifies four criteria used by mainstream economists of trade unions to judge the adequacy of their various theories. Three of them, however, are hardly ever satisfied. The root cause of this failure is the method adopted by mainstream economic theory, namely deductivism. The perspective of critical realism is used to identify and explain the cause of this failure and to point towards an alternative method.
Can the Level of Employment be Explained by GDP Growth in Transition Countries? (Theory versus the Quality of Data)
Authors: Saget Catherine
Published: March 2003

This paper presents a survey on the relationship between growth and employment in selected transition economies. The second section discusses the empirical evidence using two sets of recent data on employment and pays special attention to gender inequality in economic activity. The third section provides an overview of the literature on the causes of the behaviour of employment and output in transition countries. The fourth section tests the response of employment to GDP growth using data from 11 years and 11 countries. The main result of the paper shows that there seems to be no relationship between output growth and the level of employment in the post‐transition period for two countries: Bulgaria and Ukraine. The fact that both countries have one of the highest shares of unofficial economy in total output in the region is suggested as the main explanation behind this striking result.
The Impact of Income and Employment Status on Leaving Home: Evidence from the Italian ECHP Sample
Authors: Aassve Arnstein , Billari Francesco C. , Ongaro Fausta
Published: March 2003

In this paper we investigate the role economic resources play in the decision of young Italian adults to leave the parental home. This is of particular interest given that, in Italy, young people leave home considerably later than in other European countries. We use the first two waves of the Italian sample of the European Community Household Panel. We use a Heckman selection‐type probit procedure to account for left censoring and unobserved heterogeneity. We find that economic circumstances are important. In particular, we find personal income resources, as well as parental income levels, to be crucial in leaving home. Our results suggest that stable employment is an important prerequisite for men to start their own household. For women, on the other hand, finding a partner seems to the most important factor in becoming independent of their parents.
The Duration of Litigation
Authors: Spurr Stephen J.
Published: March 2003

This paper analyzes the duration of litigation. The analysis of court congestion by Posner (1972) and Priest (1989) suggests that the effects of delay reduction programs may be only transitory, because initial improvements may be swamped by an offsetting increase in demand for litigation. However, we find some evidence that time to settlement was reduced in a Michigan court by a program that provided for early intervention in each case by a judge who imposed a time schedule on major events of the litigation.

Using two new data sets on personal injury claims, we find that the time to settlement increases with the amount at stake but is sharply reduced when the case is referred to a specialist in personal injury litigation. Estimates of a duration model indicate that the likelihood of settlement is increased by the completion of discovery and especially by the settlement conference. We find that the hazard of settlement increases as the case gets closer to trial. This finding is in accord with the “deadline effect” derived from certain bargaining models.
Introduction: Procedural Justice, Law and Policy
Authors: Machura Stefan
Published: March 2003

Over the last few years, procedural justice has become one of the most debated fields in social science and law. Modern thinking about law, democracy and public policy inevitably leads to a consideration of appropriate procedures. Images of such procedures are culturally bound and also formed by individual cognition. The word “fair” seems to encompass the most desired features of procedures.
Coming to Terms with the Past in Germany after 1945 and 1989: Public Judgments on Procedures and Justice
Authors: Karstedt Susanne
Published: March 2003

Germany presents the unique case of a society that has been forced to come to terms with its past twice within a fifty‐year period. This double experience can contribute to our understanding of the legal processes of lustration. This paper examines a largely neglected dimension of this phenomenon: justice judgments by the general population. Justice judgments about the Nuremberg Trials and denazification after 1945 are compared to analogous procedures that took place in post‐communist East Germany after 1989. The study uses two theoretical models in its comparative approach: the Leventhal model, and the “group value” model of procedural justice set forth by Lind and Tyler. The analysis is based on survey data collected by the Office of the Military Government of the U.S. (OMGUS) from 1945 to 1949, and survey data taken in East Germany from 1989 to 1994. The results lend support to the “group value” model for the specific situation of social transition. During both periods justice judgments developed according to analogous patterns. The German experience yields some important lessons for legal policies of lustration.
Assessing the Effectiveness of Juvenile Justice Reforms: A Closer Look at the Criteria and the Impacts on Diverse Stakeholders
Authors: Mears Daniel
Published: March 2003

Research to date has taken a relatively narrow view of the criteria by which the effectiveness of juvenile justice sentencing policies are to be assessed. This narrowness is particularly striking given the comprehensive “get tough” reforms that recently have been enacted in nearly every state. Drawing on previous research and an analysis of the potential effects of a recent juvenile justice sentencing reform in Texas, this paper argues for greater attention to conceptualizing and empirically assessing effectiveness broadly, including reference to intended and unintended effects, multiple goals and means, and diverse stakeholders. The argument is sustained first by outlining and discussing these key dimensions and then by empirically illustrating the potential importance of one of these dimensions – diverse stakeholders and their respective interests.
Implementation Between Tradition and Management: Structuration and Styles of Implementation
Authors: Terpstra Jan , Havinga Tetty
Published: March 2003

This article presents a diachronic perspective for implementation research. It analyzes implementation practices in relation to their changing institutional context. Therefore, a comparison is made between different styles of implementation.

The relationship between implementation practices and institutional context is analyzed as a structuration process, following Giddens’s theory. Four styles of policy implementation are distinguished: a traditional, bureaucratic, professional, and managerial style. These four styles are connected with different phases in the development of the welfare state. This developmental model is illustrated with an analysis of policy implementation in Dutch public assistance between 1950 and the early 1990s.
Implementing the Key Components of Specialized Drug Treatment Courts: Practice and Policy Considerations
Authors: Olson David , Lurigio Arthur , Albertson Stephanie
Published: March 2003

The perceived need for specialized drug courts emerged from the most recent “war on drugs.” Courts were no longer able to handle such cases effectively because of an overwhelming volume of drug arrests and prosecutions. The increased emphasis on drug enforcement also revealed that many of the most serious criminally involved drug‐using offenders were undeterred by threats of incarceration, but were amenable to substance abuse treatment. Drug court professionals have identified several “key components” that must be in place for these courts to achieve their goals of reducing drug use and crime. Through the lens of these key drug court components, we examine the development and initiation of specialized drug treatment courts in Cook County (Chicago), Illinois. By exploring and documenting Cook County’s experiences, we elucidate several of the basic policy and organizational issues surrounding the implementation and operations of specialized drug treatment courts in the United States.
Using Student Risk Factors in School Violence Surveillance Reports: Illustrative Examples for Enhanced Policy Formation, Implementation, and Evaluation
Authors: Furlong Michael J. , Morrison Gale , Austin Gregory , Huh‐Kim Jeannie , Skager Rodney
Published: March 2003

This article presents information about the range and type of violent and associated behaviors that occur on American school campuses. We argue that the prevailing practice of reporting single‐item population estimate trends from school violence surveillance surveys provides an incomplete basis upon which to form and evaluate public policy related to school violence. The presentation draws upon information derived from the 1993, 1995, 1997, and 1999 Centers for Disease Control’s Youth Risk Behavior Surveillance (YRBS) surveys (CDC 1995, 1996, 1998, 2000) and the 1997–1998 California Student Survey (CSS) (Austin et al. 2001). Secondary analyses using the YRBS and the CSS are used to illustrate the importance of considering risk and school adjustment patterns when examining school violence trends to formulate public policy agendas.
Moving Beyond Command‐and‐Control: Reflexivity in the Regulation of Occupational Safety and Health and the Environment
Authors: Aalders Marius , Wilthagen Ton
Published: March 2003

Direct or “command‐and‐control” regulation has had limited success in dealing with occupational health and safety and with environmental regulation. This lack of success has led policymakers to experiment with self‐regulation as an alternative means of achieving the goals of social regulation. The economic subsystem fails to acknowledge its social identity and, therefore, appears to be blind to its negative performance regarding the environment and the workplace. The authors of this paper argue that moving beyond command‐and‐control can be feasible and desirable, at least to a certain extent, but that pitfalls are omnipresent. “Regulatory dilemmas” need to be solved, sound empirical studies need to be conducted, and a guiding theory needs to be drafted. To achieve these goals, the authors suggest use of the key concept of “reflexivity,” which refers to the economic organization’s relationship with itself. The practical usefulness of this theoretical concept is explored against the background of regulatory practice in the areas of occupational safety and health and the environment. It is concluded that a mode of reflexive administrative law requires a “negotiating government,” which adopts a mixture of strategies and learns to cope with issues like third‐party interests, access to information, and enforcement.
Common Stochastic Trends and Convergence of European Union Stock Markets
Authors: Serletis Apostolos , King Martin
Published: March 2003

Using data obtained from the OECD's monthly economic indicators, we convert measures of stock performance to real deutschmark units and present evidence on the number of common stochastic trends in ten European Union stock markets. Moreover, we measure the degree of convergence of these stock markets using the time‐varying parameter (Kalman filter) methodology suggested by Haldane and Hall (Economic Journal (1991), Vol. 101, No. 406, pp. 436–443).
Traverse in a Two‐Sector Kaleckian Model of Growth With Target‐Return Pricing
Authors: Lavoie Marc , Ramírez‐Gastón Pablo
Published: March 2003

The first purpose of this article is to extend the Kaleckian model of growth and distribution to a two‐sector framework, taking into account the fact that the investment good is a basic good. The second objective is to present an analysis of the traverse. It is shown that such a traverse exists when for instance profit margins are changed or when inducements to invest change. The Kaleckian traverse does not require the restrictions on technology that the standard Hicksian traverse needs.
Expectations and Monetary Policy: A Historical Perspective
Authors: Cesarano Filippo
Published: March 2003

Classical economists developed a surprisingly sophisticated analysis of money supply variations that, anticipating the main features of contemporary theory, emphasizes the role of information in the transmission mechanism. Drawing on the classical contributions, this paper outlines a general approach to monetary policy, treating information as a scarce commodity allocated optimally by rational agents.
The Rise and Fall of Walras’s Demand and Supply Curves
Authors: Creedy John
Published: March 2003

Walras is famous for his indication of how, in principle, demand curves can be derived from utility maximization. However, it is seldom recognized that Walras did not have partial equilibrium demand curves in mind. Instead, he concentrated on the context of exchange. The ‘general equilibrium’ demand and supply curves produced by Walras and extended by Launhardt have subsequently been almost entirely ‘lost’ in the literature, despite their central role in the analysis of exchange. They have been independently rediscovered several times, but the approach has not been systematically developed. The derivation of Walras’s curves is examined in this paper. It is suggested that the approach is potentially very useful, particularly in view of the increased use of non‐linear methods in economics.
The Asymmetric Effects of Monetary Policy: Some Results from a Macroeconometric Model
Authors: Arden Richard , Cook Steve , Holly Sean , Turner Paul
Published: March 2003

This paper offers evidence of the asymmetric effect of monetary policy on economic activity. First, asymmetric adjustment is captured in three macroeconomic relationships for investment, the consumer price deflator, inventories and house prices. These relationships are then embedded in a small macroeconometric model of the UK economy. Simulations on this model allow us to trace through the interactions of these asymmetries so that a monetary shock—measured by a change in interest rates—affects output and inflation in the short run in ways dependent both upon the sign of the shock and the initial state of the economy. A monetary easing has significantly larger effects on inflation when the economy is close to capacity compared with when it is in recession. These effects are captured by intrinsic asymmetries in the model, due to the use of the logarithm of interest rates and the logarithm of unemployment in the wage equation, as well as the asymmetries coming from the non‐linearities which we have introduced explicitly.
The Earnings and Employment Effects of Young People’s Vocational Training in Britain
Authors: Dolton P.J. , Makepeace G.H. , Gannon B.M.
Published: March 2003

We examine the longer run effects of youth training using the Youth Cohort Study Cohort III. These data follow individuals up to the age of 23 while previous studies typically analyse younger people. The problem of attrition is addressed by using an ‘item non‐response’ variable as an instrument to predict drop‐out. We estimate earnings and employment equations to analyse the effects of training. The results contrast with those from previous studies by suggesting there are no adverse employment or earnings effects from government‐sponsored training. We find significant returns to quality training such as degrees and apprenticeships.
Efficiency in Banking: Empirical Evidence from the Savings Banks Sector
Authors: Carbo S. , Gardener E. P. M. , Williams J.
Published: March 2003

This study aims to contribute to the established literature by using the Fourier flexible functional form and stochastic cost frontier methodologies to estimate scale economies and X‐inefficiencies for a large sample of European savings banks between 1989 and 1996. In their extensive review of the bank efficiency literature, Berger and Humphrey note that the volume of European studies has not matched that of the USA and there exists a paucity of cross‐country studies. Whereas scale economies are widespread and positively related to bank size, we find no evidence of a significant relationship between size and X‐efficiency. Generally, scale economies are found to range between 7 and 10 per cent, while X‐inefficiency measures appear to be much larger, around 22 per cent. These results suggest that European savings banks can obtain cost reductions through reducing managerial and other inefficiencies and also by increasing the scale of production.