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Article
Publication date: 17 August 2012

Steve Moffatt, Wai‐Yin Wan and Don Weatherburn

The purpose of this paper is to determine whether trends in arrests for heroin, amphetamine‐type substances (ATS) and cocaine can be used as indicators of trends in the use of…

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Abstract

Purpose

The purpose of this paper is to determine whether trends in arrests for heroin, amphetamine‐type substances (ATS) and cocaine can be used as indicators of trends in the use of these drugs.

Design/methodology/approach

The question was addressed using ARIMA models to analyse the relationship between arrests and emergency department (ED) admissions for narcotics, amphetamine type substances (ATS) and cocaine.

Findings

Strong positive correlations were found for the narcotics and cocaine series between arrests and EDs in the same month (contemporaneous correlation) and between arrests in the current month and overdoses in earlier months (lagged correlation). The contemporaneous correlation between ATS arrests and EDs was slightly less strong than the lagged correlations at two and four months. A jump in ATS EDs, was followed by a jump in arrests in the same month and then two and four months later.

Practical implications

Arrests for narcotics use/possession, ATS use/possession and cocaine use/possession may in some circumstances provide useful intelligence about drug trends and/or a basis for evaluating the impact of police drug law enforcement activity on the use of narcotics, ATS and cocaine when other stronger measures of drug use are not available.

Originality/value

Efforts to evaluate local drug law enforcement activity on illicit drug use have been hampered by poor measures of trends in illicit drug use at small area levels. This is the only study the authors are aware of that has examined the long‐term relationship between illicit drug arrests and emergency department admissions for illicit drug use.

Details

Policing: An International Journal of Police Strategies & Management, vol. 35 no. 3
Type: Research Article
ISSN: 1363-951X

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Book part
Publication date: 1 January 2008

S.T. Boris Choy, Wai-yin Wan and Chun-man Chan

The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is replaced by the Student-t distribution for robustness…

Abstract

The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is replaced by the Student-t distribution for robustness consideration. The model is then called the t-t SV model throughout this paper. The objectives of the paper are twofold. First, we introduce the scale mixtures of uniform (SMU) and the scale mixtures of normal (SMN) representations to the Student-t density and show that the setup of a Gibbs sampler for the t-t SV model can be simplified. For example, the full conditional distribution of the log-volatilities has a truncated normal distribution that enables an efficient Gibbs sampling algorithm. These representations also provide a means for outlier diagnostics. Second, we consider the so-called t SV model with leverage where the observations and log-volatilities follow a bivariate t distribution. Returns on exchange rates of Australian dollar to 10 major currencies are fitted by the t-t SV model and the t SV model with leverage, respectively.

Details

Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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Book part
Publication date: 1 January 2008

Abstract

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Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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Book part
Publication date: 1 January 2008

Siddhartha Chib, William Griffiths, Gary Koop and Dek Terrell

Bayesian Econometrics is a volume in the series Advances in Econometrics that illustrates the scope and diversity of modern Bayesian econometric applications, reviews some recent…

Abstract

Bayesian Econometrics is a volume in the series Advances in Econometrics that illustrates the scope and diversity of modern Bayesian econometric applications, reviews some recent advances in Bayesian econometrics, and highlights many of the characteristics of Bayesian inference and computations. This first paper in the volume is the Editors’ introduction in which we summarize the contributions of each of the papers.

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Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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Book part
Publication date: 2 April 2021

Shuhan Chen and Peter Lunt

Free Access. Free Access

Abstract

Details

Chinese Social Media
Type: Book
ISBN: 978-1-83909-136-0

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