THE FUTURE TRAJECTORY OF U.S. CO2 EMISSIONS: THE ROLE OF STATE VS. AGGREGATE INFORMATION*

ABSTRACT This paper provides comparisons of a variety of time‐series methods for short‐run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state‐level data set from 1960–2001. We test the out‐of‐sample performance of univariate and multivariate forecasting models by aggregating state‐level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in‐sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a “greener” median voter are more likely to push toward voluntary cutbacks in emissions.     

Analyzing Hotspots of Crime Using a Bayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in the Greater Toronto Area

Conventional methods used to identify crime hotspots at the small‐area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small‐area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area‐specific violent crime trends for small‐area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area‐specific trends in the north and decreasing area‐specific trends in the southeast. Posterior probabilities of area‐specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective.     

FORECASTING METROPOLITAN EMPLOYMENT USING AN EXPORT-BASE ERROR-CORBECTION MODEL

This paper shows that a time-series version of export-based location quotients, which decompose area employment into export and local components are co-integrated. Given the statistically established co-integrating relation between the time series for export and local employment, the Granger Representation Theorem tells us that an error-correction mechanism model is the appropriate time-series model specification. A set of forecasting experiments is carried out, in order to test whether the error-correction model produces better long-run forecasts that hold together in economically meaningful ways. Given the computer hardware and software that currently exist, the model developed here represents a simple, low-cost method for accurately forecasting local-area employment in the tradition of past work on export-base models.     

Geographical and Temporal Weighted Regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19‐year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling.     

New Statistical Approaches to Intra‐individual Isotopic Analysis and Modelling of Birth Seasonality in Studies of Herd Animals

This paper introduces improved methods for statistically assessing birth seasonality and intra‐annual variation in δ¹⁸O from faunal tooth enamel. The first method estimates input parameters for use with a previously developed parametric approach by C. Tornero et al. The second method uses a non‐parametric clustering procedure to group individuals with similar time‐series data and estimate birth seasonality. This method was successful in analysing data from a modern sample with known season of birth, as well as two heterogeneous archaeological data sets. Modelling indicates that the non‐parametric approach estimates birth seasonality more successfully than the parametric method when less of the tooth row is preserved. The new approach offers a high level of statistical rigour and flexibility in dealing with the time‐series data produced through intra‐individual sampling in isotopic analysis.     

Sensuous Solidarities: Emotion,Politics and Performance in the Clandestine Insurgent Rebel Clown Army

Abstract: This paper is concerned with the political performance of the Clandestine Insurgent Rebel Clown Army (CIRCA) during the protests against the G8 meeting in Gleneagles, Scotland in 2005. In particular, the paper is concerned with how emotional experiences within political moments or events can be constituted through performances that fashion “sensuous solidarities”. Sensuous solidarities are generated through diverse bodily movements and techniques, and are indicative of both the performative character of activist subjectivities and the content of activists’ public (political) performances. Reflecting on my participation in CIRCA, this paper will argue that sensuous solidarities constituted a series of complex, contradictory and emotive co‐performances and resonances with police, other protestors and the public and in doing so will consider the efficacy of those forms of activism that Duncombe (2007, Dream: Reimagining Progressive Politics in an Age of Fantasy. London: The New Press) has termed “ethical spectacles”.     

Leading Indicators and Spatial Interactions: A Crime-Forecasting Model for Proactive Police Deployment

We develop a leading indicator model for forecasting serious property and violent crimes based on the crime attractor and displacement theories of environmental criminology. The model, intended for support of tactical deployment of police resources, is at the microlevel scale; namely, 1-month-ahead forecasts over a grid system of 141 square grid cells 4000 feet on a side (with approximately 100 blocks per grid cell). The leading indicators are selected lesser crimes and incivilities entering the model in two ways: (1) as time lags within grid cells and (2) time and space lags averaged over grid cells contiguous to observation grid cells. Our validation case study uses 1.3 million police records from Pittsburgh, Pennsylvania, aggregated over the grid system for a 96-month period ending in December 1998. The study uses the rolling-horizon forecast experimental design with forecasts made over the 36-month period ending in December 1998, yielding 5076 forecast errors per model. We estimated the leading indicator model using a robust linear regression model, a neural network, and a proven univariate, extrapolative forecast method for use as a benchmark in Granger causality testing. We find evidence of both the crime attractor and displacement theories. The results of comparative forecast experiments are that the leading indicator models provide acceptable forecasts that are significantly better than the extrapolative method in three out of four cases, and for the fourth there is a tie but poor forecast performance. The leading indicators find 41–53% of large crime volume changes in the three successful cases. The corresponding workload for police is quite acceptable, with on the average 5.2 potential large change cases per month to investigate and with 31% of such cases being positives.     

Social capital determinants and labor market networks

We explore the links between determinants of social capital and labor market networks at the neighborhood level. We harness rich data taken from multiple sources, including matched employer–employee data with which we measure the strength of labor market networks, data on neighborhood homogeneity that has previously been tied to social capital, and new data—not previously used in the study of social capital—on the number and location of nonprofit sector establishments at the neighborhood level. We use a machine learning algorithm to identify the potential determinants of social capital that best predict neighborhood‐level variation in labor market networks. We find evidence suggesting that smaller and less centralized schools, and schools with fewer poor students, foster social capital that builds local labor market networks, as does a larger Republican vote share. The presence of establishments in a number of nonprofit‐oriented industries are identified as predictive of strong labor market networks, likely because they either provide public goods or facilitate social contacts. These industries include, for example, churches and other religious institutions, fire and rescue services including volunteer fire departments, country clubs and golf courses, labor unions, chamber music groups, hobby clubs, and schools.     

Politicians, Parishes and Police: The Failure of the 1812 Night Watch Bill

The public outcry heard in the wake of the Ratcliffe Highway murders of December 1811 was muted by May 1812 when the 1812 Night Watch Bill died in the house of commons. Responding to the moral panic following the murder of two East End families, the home office gathered considerable information and input from the professional police magistrates and local authorities before proposing the reform of parochial night watch in much of metropolitan London. Nevertheless the bill ran into concerted opposition on grounds of practicality as well as of ideology. A close study of its trajectory through parliament illuminates the role of parliament as a broker for conflicting demands emanating from differing concepts of the public good. The failure of the Night Watch Bill adds significantly to our understanding of the genesis of legislative initiatives, calling into question whether it is possible to distinguish accurately whether particular bills originated from back- or front-bench activity as well as to our knowledge of the relationship between parliamentary activity, ministerial objectives and public opinion.     

Aggregation of Regional Economic Time Series with Different Spatial Correlation Structures. 具有不同空间相关结构的区域经济时间序列集成

In this article, we compare the relative efficiency of different forecasting methods of space‐time series when variables are spatially and temporally correlated. We consider two cases: (1) univariate forecasting (i.e., a space‐time series aggregated into a single time series) and (2) the more general instance of multivariate forecasting (i.e., a space‐time series aggregated into a coarser spatial partition). We extend the results in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. We first introduce a statistical framework based on the space‐time autoregressive class of random field models, which constitutes the basis of our simulation study, and we present the various alternative forecasting methods considered in the simulation. We then present the results of a Monte Carlo study related to univariate forecasting. In order to allow a comparison with the findings of Giacomini and Granger (2004), we consider the same forecasting strategies and the same combinations of the parameter values used there, but with a larger parametric set. Finally, we extend our analysis to the case of multivariate forecasting. The outcomes obtained provide operational suggestions about how to choose between alternative forecasting methods in empirical circumstances.     

Rural communities and small area forecasting: some examples from South Australia

This paper demonstrates and evaluates two different but complementary methodologies of small area forecasting in a rural area some 80 km north of Adelaide. The first approach forecasts overall population trends using a sector-by-sector appraisal of the growth/decline prospects of each economic sector based on detailed field surveys carried out in 1968 and 1970. The second approach was to use the readily available 1971 census and corresponding vital statistics data to project the population of the study area using a simple cohort-component projection methodology. Both approaches made forecasts of expected population levels by 1980, and in that year the authors conducted a resurvey of the same study area. The article demonstrates the need for, and results of, regular updating of the assumptions on which population forecasts are made, for the period 1968–1980 includedan unexpected revival of the farm economy as well as the onset of the international urban-rural migration flow of the ‘population turnaround’. Neither of these trends had been foreseen in the initial forecasts; their effects are shown in the results of the 1980 resurvey, and the utility of the forecasting methodologies is discussed in the light of these findings.     

A ROBUST APPROACH TO PREDICTING FLUCTUATIONS IN STATE-LEVEL EMPLOYMENT GROWTH*

ABSTRACT. Economic forecasting models are famous for performing well over short time periods and then suffering rapidly deteriorating performance when economic conditions change. This behavior makes composite forecasting models valuable in situations where large forecast errors cause considerable losses. A composite forecasting model for state-level employment is proposed here. This method is designed to protect state budget processes by producing robust forecasts of changes in employment and the related revenue collections. An application to Georgia nonagricultural employment is presented which demonstrates the benefits of this technique. The example shows that the method can forecast such series accurately without the forecaster having to choose in advance a single model specification to all economic conditions.     

New approaches to economic development in peripheral rural regions

This paper explores new approaches to economic development in peripheral regions in the context of constraints on public expenditure, declining employment in traditional natural resource based industries, and globalisation of the economy. Three conceptual pairs ‐resource mobility and immobility; tangible and intangible factors; and global‐local interrelations — underpin three ideas about these new approaches, and their impact on differential economic performance observed in otherwise similar localities and regions. A case study is given to illustrate the role played by less mobile cultural, social and environmental assets in these strategies. However, a key feature of the cases is the importance of both local and extra‐local linkages, often at international level, whether this has to do with market or non‐market activity. The conclusions raise questions for research about the root causes of differences in economic performance between rural localities, whether local initiatives will suffice to counter further likely declines in public subventions and natural resource based employment and also about the focus of policy in such regions.     

A SPATIAL AND TEMPORAL AUTOREGRESSIVE LOCAL ESTIMATION FOR THE PARIS HOUSING MARKET

ABSTRACT This original study examines the potential of a spatiotemporal autoregressive Local (LSTAR) approach in modeling transaction prices for the housing market in inner Paris. We use a data set from the Paris Region notary office (Chambre des notaires d’Île‐de‐France) which consists of approximately 250,000 transactions units between the first quarter of 1990 and the end of 2005. We use the exact X–Y coordinates and transaction date to spatially and temporally sort each transaction. We first choose to use the STAR approach proposed by Pace et al., 1998 . This method incorporates a spatiotemporal filtering process into the conventional hedonic function and attempts to correct for spatial and temporal correlative effects. We find significant estimates of spatial dependence effects. Moreover, using an original methodology, we find evidence of a strong presence of both spatial and temporal heterogeneity in the model. It suggests that spatial and temporal drifts in households socio‐economic profiles and local housing market structure effects are certainly major determinants of the price level for the Paris Housing Market.     

CRIME IN A MULTI‐JURISDICTIONAL MODEL WITH PRIVATE AND PUBLIC PREVENTION*

ABSTRACT Criminals move between jurisdictions in response to differences in the net returns to crime that depend on the opportunity for crime and the effort to prevent crime. An increase in police protection of a jurisdiction diverts crime to other jurisdictions when only public crime prevention such as police protection is available. However, residents also invest in private prevention (private security, burglar alarms, etc.), and the value of these measures depends on the level of local public protection. In a spatial context, an increase in public prevention of a jurisdiction not only alters the incentives of individuals of the jurisdiction, but also of other jurisdictions as well, and such a change in private crime prevention may end up attracting crime to the jurisdiction. An increase in public prevention of a jurisdiction thus may divert or attract crime. This ambiguous effect stands in contrast with the literature and may appear counterintuitive, but is logical under plausible conditions.     

Forecasting in a Small and Unstable Regional Economy Using Regime Shifting Models: The Case of Extremadura

We consider forecasting in a small and unstable regional economy subject to structural breaks. In this context, we work with two types of regime‐shifting databased models using cointegration theory. The objective of the present work is to analyze the out‐of‐sample forecasting performance of the two approaches used to construct a short‐term regional econometric model: stochastic and deterministic time varying parameters models. The forecasting experiments will be illustrated by specifying, and estimating an econometric model for Extremadura, a small and unstable region in southwestern Spain.     

On the Spatial Decomposition of Forecasts

This paper uses historical socioeconomic data to evaluate the elasticities of a Dendrinos‐Sonis one‐population/two‐locations nonlinear dynamic comparative advantage model for determining subregional shares of aggregate regional forecasts. The fractal dimension properties of the discrepancies between the actual and simulated data are used to enhance the forecasting framework. The analysis focuses in the first phase on total population, total personal income, and earnings by sector for the Columbus, Ohio Metropolitan Statistical Area, and Delaware County, one of its component counties. For the second phase of the analysis, these data for nondurable goods and for services are used, along with monthly data for total employment in Columbus, Cincinnati, and Cleveland, Ohio. Annual data for the analysis are drawn from the U. S. Bureau of Economic Analysis, Regional Economic Information System, while the monthly data come from the Ohio Bureau of Employment Securities. The nonlinear dynamic model is shown to outperform conventional approaches for the majority of socioeconomic stocks.     

New approaches to deterrence in Britain, France, and the United States

The three western nuclear powers have in recent years been more preoccupied with threats from regional powers armed with weapons of mass destruction than with potential major power threats. London, Paris, and Washington have each substantially reduced their deployed nuclear forces and sharply cut back their range of delivery systems since the end of the Cold War in 1989‐1991. While each has manifested greater interest in non‐nuclear capabilities for deterrence, each has attempted, with varying degrees of clarity, to define options for limited nuclear use. All three have articulated their nuclear employment threats within a conceptual framework intended to promote deterrence. Despite the differences in their approaches and circumstances, the three western nuclear powers are grappling with tough and, to some extent, unanswered questions: what threat will deter? To what extent have the grounds for confidence in deterrence been diminished? To what extent has it been prudent to scale back deployed nuclear capabilities and redefine threats of nuclear retaliation? To what extent would limited nuclear options enhance deterrence and simplify nuclear employment decisions? What level of confidence should be placed in the full array of deterrence and containment measures? To what extent is deterrence national policy, and to what extent is it Alliance policy?     

The quality of (in)justice in London's magistrates' courts

Concerns about structural racism and policing have fuelled public demands in the US, UK and elsewhere to ‘defund’ and demilitarize the police. However, given the interlinked nature of the entire criminal justice system – the police, courts and prisons – further thought needs to be given to how the system should be reformed. This article summarizes research on the nature of injustice experienced in magistrates’ courts in London from the point of view of defendants from black, Asian and ethnic minority communities and ‘the precariat’.     

The last laugh: Western Australia's elections and the executive state, 1983–89

Aside from bribery and corruption associated with the police and organized ‘vice and crime’, Mr Tony Fitzgerald Q.C. highlighted in his ‘Report’ five criteria as areas of concern over the health and future of parliamentary democracy in Queensland. They were: 1. Decline of Parliament in terms of (a) sitting hours, and (b) consistent refusal by the Premier and Ministers to answer parliamentary questions and to be fully accountable and responsible to Parliament for public expenditures, ministerial expenses and extra‐parliamentary executive decisions; 2. Business deals, joint ventures and other financial transactions between government, its agencies and government favourites; 3. Lavish funding of the governing political party by recipients of its favours; 4. Political ‘stacking’ of the public service; 5. Use of taxpayers’ funds by Premier and Ministers to finance writs against critics.