Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

The adequacy of asymptotic approximations in the near-integrated autoregressive model with dependent errors

We consider the normalized least squares estimator of the parameter in a nearly integrated first-order autoregressive model with dependent errors. In a first step we consider its asymptotic distribution as well as asymptotic expansion up to order O_p(T⁻¹). We derive a limiting moment generating function which enables us to calculate various distributional quantities by numerical integration. A simulation study is performed to assess the adequacy of the asymptotic distribution when the errors are correlated. We focus our attention on two leading cases: MA(1) errors and AR(1) errors. The asymptotic approximations are shown to be inadequate as the MA root gets close to −1 and as the AR root approaches either −1 or 1. Our theoretical analysis helps to explain and understand the simulation results of Schwert (1989) and DeJong, Nankervis, Savin, and Whiteman (1992) concerning the size and power of Phillips and Perron's (1988) unit root test. A companion paper, Nabeya and Perron (1994), presents alternative asymptotic frameworks in the cases where the usual asymptotic distribution fails to provide an adequate approximation to the finite-sample distribution.     

A new continuous distribution and two new families of distributions based on the exponential

Recent work on social status led to derivation of a new continuous distribution based on the exponential. The new variate, termed the ring(2)-exponential, in turn leads to derivation of two closely related new families of continuous distributions, the mirror-exponential and the ring-exponential. Both the standard exponential and the ring(2)-exponential are special cases of both the new families. In this paper, we first focus on the ring(2)-exponential, describing its derivation and examining its properties, and next introduce the two new families, describing their derivation and initiating exploration of their properties. The mirror-exponential arises naturally in the study of status; the ring-exponential arises from the mathematical structure of the ring(2)-exponential. Both have the potential for broad application in diverse contexts across science and engineering. Within sociobehavioral contexts, the new mirror-exponential may have application to the problem of approximating the form and inequality of the wage distribution.     

A new bivariate distribution in natural exponential family

We propose a new bivariate distribution following a GLM form i.e., natural exponential family given the constantly correlated covariance matrix. The proposed distribution can represent an independent bivariate gamma distribution as a special case. In order to derive the distribution we utilize an integrating factor method to satisfy the integrability condition of the quasi-score function. The derived distribution becomes a mixture of discrete and absolute continuous distributions. The proposal of our new bivariate distribution will make it possible to develop some bivariate generalized linear models. Further the discrete correlated bivariate distribution will also arise from an independent bivariate Poisson mass function by compounding our proposed distribution (Iwasaki and Tsubaki, 2002).Received March 2003     

中国经济增长空间相关性研究——基于"长三角"与"珠三角"的实证

本文建立了两套回归模型:无空间相关变量的单向和双向固定效应模型;有空间相关变量的单向和双向固定效应模型。对比分析后得出的结论有:"长三角"和"珠三角"区域内部的确存在经济增长的空间相关性,"珠三角"区域内部经济增长的空间相关性略大于"长三角"区域,但两者在统计上并没有显著的差别;两个地区都存在经济增长的β收敛,但"珠三角"地区呈现出更强的收敛性;对于影响"长三角"和"珠三角"经济增长的因素,有些相同和不同点值得关注。     

中国通货膨胀成因的实证分析

通过对可能影响我国通货膨胀的因素,包括经济增长、货币供应量、居民消费水平和工资的格兰杰因果分析和自回归分布滞后模型的拟合,可知我国通货膨胀和货币供应量、居民预期有密切关系,而和其他因素没有显著关系.     

我国最低工资政策的就业效应——基于北京市和重庆市建筑业的分析

VAR模型对最低工资就业效应的分析结果显示:北京市的最低工资标准与建筑业就业只是在数理统计上存在关系,并没有实际的经济意义;最低工资对重庆市建筑业的微弱负影响可以忽略不计。最低工资的就业效应并不明显,可以紧密联系行业平均工资来调整最低工资标准,以保障劳动者的基本生活。     

ON VALUING STOCHASTIC PERPETUITIES USING NEW LONG HORIZON STOCK PRICE MODELS DISTINGUISHING BOOMS,BUSTS, AND BALANCED MARKETS

For longer horizons, assuming no dividend distributions, models for discounted stock prices in balanced markets are formulated as conditional expectations of nontrivial terminal random variables defined at infinity. Observing that extant models fail to have this property, new models are proposed. The new concept of a balanced market proposed here permits a distinction between such markets and unduly optimistic or pessimistic ones. A tractable example is developed and termed the discounted variance gamma model. Calibrations to market data provide empirical support. Additionally, procedures are presented for the valuation of path dependent stochastic perpetuities. Evidence is provided for long dated equity linked claims paying coupon for time spent by equity above a lower barrier, being underpriced by extant models relative to the new discounted ones. Given the popularity of such claims, the resulting mispricing could possibly take some corrections. Furthermore for these new discounted models, implied volatility curves do not flatten out at the larger maturities.     

中国居民消费水平与经济增长关系的实证分析

运用协整理论、自回归分布滞后模型、修正误差模型对中国1952年～2009年居民消费与经济增长之间的关系进行了分析,结果表明:经济增长对居民的消费有促进作用,且存在长期的均衡关系,改革开放后还大于改革开放前。     

The causality examination between demand uncertainty and hotel failure: A case study of international tourist hotels in Taiwan

The hotel business is highly sensitive to economic cycles, as the industry faces high frequency and high fluctuation of uncertainty over the demand for lodging services. Without a thorough consideration on the issue, hotels may undergo a business crisis. This research therefore investigates the influence of demand uncertainty on hotel failure by using the operation data of international tourist hotels in Taiwan during 1995–2008. The analysis applies two stages of estimation. The first stage employs a first-order autoregressive model, AR(1), to model lodging demand uncertainty. The second stage estimates the likelihood of hotels’ failure by using a logit model. The results are supportive to determine that the demand uncertainty causes hotel failures.