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.     

Simpler Probabilistic Population Forecasts: Making Scenarios Work

The traditional high-low-medium scenario approach to quantifying uncertainty in population forecasts has been criticized as lacking probabilistic meaning and consistency. This paper shows, under certain assumptions, how appropriately calibrated scenarios can be used to approximate the uncertainty intervals on future population size and age structure obtained with fully stochastic forecasts. As many forecasting organizations already produce scenarios and because dealing with them is familiar territory, the methods presented here offer an attractive intermediate position between probabilistically inconsistent scenario analysis and fully stochastic forecasts.     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

灰色理论在城市总体规划中的应用研究

在编制城市总体规划时收集的资料和信息往往是不完全和不对称的，很多数据需要通过科学的理论去建立合理的模型来推算，本文采用灰色理论建模，以GDP预测为例详细说明了灰色预测模型GM（1，1）的建模过程。该模型对于预测城市总体规划中水文、人口、用地规模、城市化水平、交通运输发展、公共交通以及客货运量等具有普遍指导意义。     

Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model

Mortality forecasting has crucial implications for insurance and pension policies. A large amount of literature has proposed models to forecast mortality using cross-sectional (period) data instead of longitudinal (cohort) data. As a consequence, decisions are generally based on period life tables and summary measures such as period life expectancy, which reflect hypothetical mortality rather than the mortality actually experienced by a cohort. This study introduces a novel method to forecast cohort mortality and the cohort life expectancy of non-extinct cohorts. The intent is to complete the mortality profile of cohorts born up to 1960. The proposed method is based on the penalized composite link model for ungrouping data. The performance of the method is investigated using cohort mortality data retrieved from the Human Mortality Database for England & Wales, Sweden, and Switzerland for male and female populations.     

Evaluation of the social and economic impact of carbon dioxide (CO2) emissions on sustainable mobility using cumulative ordinal models: trend odds model

In the context of environmental sustainability evaluation, grouped under climate change, health and ecosystem themes, an impact that could be investigated is the Global Warming Potential (GWP), whose sources are a multitude. In urban areas the evaluation of real vehicles emissions is an essential activity in order to suggest possible solution to local administrators. They still express the need to improve and maintain the characteristics of the breathing air at the best possible quality level. Moreover, these solutions, such as planning measures or traffic control management in respect of pollution, would be apply at different geographical levels, i.e. national, regional or urban scale. Another factor to be investigated is the effect of technologies and emission control systems to comply more stringent limits (Euro 4/5/6) and improve air quality to a lower environmental impact. GWP, indicator of climate change, is measured in terms of CO₂ equivalency emission values variable. To perform this activity an experimental campaign was carried out with several vehicles from different manufacturers and with a wide variety in terms of mass, power, engine displacement and type approval technology. The experimental plan includes some repetitions of the urban section in Naples city centre.The purpose of this paper is twofold: first, to provide a strategy on the choice of a logistic model with ordinal data and with trend, and, second, to evaluate the usefulness of such models for environmental sustainability (CO₂ and other vehicular pollutant emissions), with particular emphasis on model formulation, the interpretation of model coefficients, and the implications of such models.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Threshold models for trended time series

This paper presents the theoretical development of a new threshold autoregressive model based on trended time series. The theoretical arguments underlying the model are outlined and a nonlinear economic model is used to derive the specification of the empirical econometric model. Estimation and testing issues are considered and analysed. Additionally we apply the model to the empirical investigation of U.S. GDP.This paper is the result of work carried out for the author's Ph.D. thesis. I would like to thank Hashem Pesaran for his help, encouragement and insights during the preparation of this paper. I also thank Gary Koop and Sean Holly for helpful comments. Financial assistance from the Economic and Social Research Council is gratefully acknowledged.     

Asymmetric ACD models: Introducing price information in ACD models

This paper proposes an asymmetric autoregressive conditional duration (ACD) model, which extends the ACD model of Engle and Russell (1998). The asymmetry consists of letting the duration process depend on the state of the price process. If the price has increased, the parameters of the ACD model can differ from what they are if the price has decreased. The model is applied to the bid-ask quotes of two stocks traded on the NYSE and the evidence in favour of asymmetry is strong. Information effects (Easley and O'Hara 1992) are also empirically relevant. As the model is a transition model for the price process, it delivers `market forecasts' of where prices are heading. A trading strategy based on the model is implemented using tick-by-tick data.While remaining responsible for any error in this paper, the authors would like to thank R. Anderson, G. Le Fol, C. Gouriéroux, J. Jasiak, W. Pohlmeier, A. Roell, O. Scaillet, S. Wei and three anonymous referees for useful remarks and suggestions on previous versions. The authors would also like to thank A. Ruttiens from KBC-CBC for useful discussions on practical issues related to trading. Support of the European Commission Human Capital and Mobility Program through the network `Econometric inference using simulation methods' is gratefully acknowledged. This paper presents research results of the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian State, Prime Minister's Office, Science Policy Programming. The scientific responsibility is assumed by the authors.     

Noisy chaotic dynamics in commodity markets

The nonlinear testing and modeling of economic and financial time series has increased substantially in recent years, enabling us to better understand market and price behavior, risk and the formation of expectations. Such tests have also been applied to commodity market behavior, providing evidence of heteroskedasticity, chaos, long memory, cyclicity, etc. The present evaluation of futures price behavior confirms that the resulting price movements can be random, suggesting noisy chaotic behavior. Prices could thus follow a mean process that is dynamic chaotic, coupled with a variance that follows a GARCH process. Our conclusion is that models of this type could be constructed to assist in forecasting prices in the short run but not over long run time periods.First version received: June 2001/Final version received: March 2003