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This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics. - ;Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are n

We adopt a structural time series analysis to investigate the impact of parole abolition and sentence reform in Virginia on reported crime rates. The Commonwealth of Virginia abolished parole and reformed sentencing for all felony offences committed on or after January 1, 1995. To examine the impact of Virginia's change in legislation on reported crime rates from 1995 onwards, we perform an intervention time series analysis based on structural time series models. We empirically find that the change in legislation has significantly reduced the burglary rates and to a lesser extent the murder rates in Virginia. For other violent crimes such as rape and aggravated assault the evidence of a significant reduction in crime rates is less evident or is not found. This empirical study for Virginia also provides an illustration of how an effective intervention time series analysis can be carried out in crime studies.

Risk is at the center of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning counter- measures, the risk and severity of counterparty default concerns bank risk managers on a daily basis and the risk of infection has actuarial and epidemiological consequences. However, risk can not be observed directly and it usually varies over time. Measuring risk is therefore an important exercise. In this paper we introduce a general multivariate framework for the time series analysis of risk that is modelled as a latent process. The latent risk time series model extends existing approaches by the simultaneous modelling of (i) the exposure to an event, (ii) the risk of that event occurring and (iii) the severity of the event. First, we discuss existing time series approaches for the analysis of risk which have been applied to road safety, actuarial and epidemiological problems. Seco! nd, we present a general model for the analysis of risk and discuss its statistical treatment based on linear state space methods. Third, we apply the methodology to time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk.

We analyze the illicit drug usage by inhabitants and visitors of European cities. Our statistical analyses are by means of linear mixed models. The data on illicit drug usage of cocaine, ecstasy, amphetamines, methamphetamines, and cannabis are collected through wastewater samples from the inlet of 21 sewage treatment plants spread over 11 European countries. The data set represents nineteen cities, services a population of approximately 15 million inhabitants and covers a one-week period in 2011. The patterns of illicit drug usage are examined with respect to temporal (daily) and spatial variations, as well as in relation to economic wealth (gross domestic product) and criminological (drug offenses recorded by police) factors. In a joint statistical analysis, we find that cocaine and ecstasy are typically recreational drugs that are consumed during the weekend. Inhabitants of Western European countries consume more cocaine than inhabitants of Eastern European countries. This finding cannot be explained by political divisions between West and East. We also find evidence that higher usage of ecstasy is associated with medium-sized cities, economic prosperity, and a lower number of drug offenses.On the other hand, higher usage of methamphetamine is associated with medium-sized cities and low economic wealth.