Identifying Risk Groups in Flanders: Time Series Approach


 Christal Loreen Hoover
 3 years ago
 Views:
Transcription
1 Identifying Risk Groups in Flanders: Time Series Approach RAMOW D. Karlis, E. Hermans Onderzoekslijn Risicobepaling DIEPENBEEK, STEUNPUNT MOBILITEIT & OPENBARE WERKEN SPOOR VERKEERSVEILIGHEID
2 Documentbeschrijving Rapportnummer: Titel: RAMOW Identifying Risk Groups in Flanders: Time Series Approach Auteur(s): D. Karlis, E. Hermans Promotor: Prof. dr. Geert Wets Onderzoekslijn: Risicobepaling Partner: Universiteit Hasselt Aantal pagina s: 38 Projectnummer Steunpunt: 6.1 Projectinhoud: In dit project worden prognoses op vlak van verkeersveiligheid in Vlaanderen gemaakt. Uitgave: Steunpunt Mobiliteit & Openbare Werken, juni Steunpunt Mobiliteit & Openbare Werken Wetenschapspark 5 B 3590 Diepenbeek T F E I
3 Samenvatting Titel: Identificeren van risicogroepen in Vlaanderen: Tijdreeksbenadering Jaarlijkse ongevallen en blootstellingsdata voor Vlaanderen uit de periode worden gebruikt om statespace modellen op te stellen en verkeersveiligheidsvoorspellingen voor de periode te maken. We maken gebruik van het zogenaamde Latente Risico Tijdreeksmodel, dat geschikt is voor het modelleren van ongevallendata om op die manier inzicht te verkrijgen in de verkeersveiligheidssituatie die kan verwacht worden in de komende jaren. In dit model worden twee componenten, blootstelling enerzijds en verkeersdoden (of een andere categorie van verkeersslachtoffers) anderzijds, gelijktijdig beschouwd. Bovendien focussen we in de analyse ook op kleinere subgroepen, bepaald op basis van de leeftijd van het verkeersslachtoffer, het wegtype en het type weggebruiker (of zijn transportmodus). De voorspellingen duiden op een verwachte daling in het aantal verkeersdoden, hoewel dit niet aan hetzelfde tempo zal gebeuren voor de verschillende subgroepen. Steunpunt Mobiliteit & Openbare Werken 3 RAMOW
4 English summary Annual accident and exposure data from Flanders covering the period are used in order to create state space models and make road safety predictions for the period We make use of the Latent Risk time series model, suitably developed for accident data in order to forecast the road safety situation that can be expected in the forthcoming years. In this model two components, the exposure measurement and the fatalities measurement (or another category of road casualties), are fitted simultaneously. Moreover, in the analysis we also focus on smaller subgroups, depending on the age of the road traffic victim, the road type and the road user type (or transport mode). Forecasts clearly show that the number of fatalities is expected to decrease, however not at the same rate for different subgroups. Steunpunt Mobiliteit & Openbare Werken 4 RAMOW
5 Inhoudsopgave 1. INTRODUCTION STATE SPACE MODELS THE LATENT RISK MODEL RESULTS FROM AGGREGATE MODELS RESULTS FROM DISAGGREGATE MODELS By Road user type By Age category By Road type SOME STATISTICAL CONSIDERATIONS CONCLUSIONS REFERENCES APPENDIX... 32
6 1. I N T R O D U C T I O N Road traffic crashes are one of the world s largest public health and injury prevention problems. The problem is all the more important because the victims are overwhelmingly healthy prior to their crashes. A report published by the World Health Organization (WHO, 2009) estimated that approximately 1.3 million people die each year on the world's roads, between 20 and 50 million sustain nonfatal injuries and traffic accidents were the leading cause of death among children of years of age. Undoubtedly there is awareness in most societies about this issue and reducing the fatalities from road accidents is always on every political agenda. Also, the issue of traffic safety is high in the academic agenda and a lot of research is undertaken in order to examine and improve traffic safety issues. Lately, there was a downward trend in the number of fatalities in most countries in Western Europe, North America and Oceania (see Elvik, 2010, see also Lassarre, 2001), reflecting the awareness of the problem as well as all the measures undertaken to decrease it. However, apart from fatalities there is also a great concern for the public with respect to other types of nonfatal accidents as they also produce significant losses and thereby contribute to the economic costs. In Flanders, almost casualties were registered in 2009 (FOD Economie, 2011). The purpose of the current report is to forecast the (disaggregate) level of road safety in Flanders up to The data used in the analyses are yearly data up to 2007 covering different subgroups like different age categories, different road user types as well as different road types. A close look in such subgroups is also of primary importance in safety research as it can reveal vulnerable subgroups for which particular measures are most urgently needed. Our forecasts are based on a state space model developed by Bijleveld et al (2008) and is suitable for road safety data as it captures the basic ideas in road safety research. The model assumes that road casualties (of a particular severity level, e.g. fatalities) are the result of the road risk and the exposure of individuals to that risk. While exposure can be approximated using real data the risk is a latent factor not directly observable. So we make use of the Latent Risk time series model that on the one hand treats the risk as latent and on the other hand models the exposure and the casualties at the same time. The model is applied to fatalities, casualties, serious and slight injuries using data from the entire population. As exposure we use the total number of kilometres travelled (in millions). Then we focus on subgroups, namely road type, road user and age. As exposure now we use relevant detailed data if available or proxies if not. Moreover, we primarily focus on fatalities in the disaggregate analyses. The report proceeds as follows: Section 2 briefly introduces the state space models, while section 3 describes the Latent risk model used in the report. Section 4 contains the main results for the entire population. In section 5 we provide some disaggregate analysis focusing on particular subgroups of the population. Working with subgroups offers interesting challenges as for example, most measures affect only a part Steunpunt Mobiliteit & Openbare Werken 6 RAMOW
7 of the population. Section 6 deals with some statistical considerations about the model fitting. Concluding remarks can be found in section 7. Additional results have been put in the appendix. Steunpunt Mobiliteit & Openbare Werken 7 RAMOW
8 2. S T A T E S P A C E M O D E L S Road safety data are typically data observed in subsequent time points, creating, hence, a time series. The density of the observations depends on the way they are collected and can have very different time spans. In this report we consider annual data 1, which implies that seasonality has been cancelled out (and consequently, seasonality issues will not be described). A powerful class of time series models are the dynamic models, i.e. models where the parameters may change over time. There are two main classes of univariate dynamic models: ARIMA models studied by Box and Jenkins and unobserved component models which are also called structural models, by Harvey and Sheppard (1993). In a structural model each component or equation is intended to represent a specific feature or relationship in the system under study. State space methods described in this section, belong to the latter group of models. A typical time series may be decomposed in a trend, a seasonal and an irregular part. An important characteristic is that the components are stochastic. Models without stochastic component are called static. Moreover, explanatory variables can be added and intervention analysis carried out. The principal structural time series models are therefore nothing more than regression models in which the explanatory variables are functions of time and the parameters are timevarying. The key to handle structural time series models is the state space form, with the state of the system representing the various unobserved components. State space time series analysis began with the path breaking paper of Kalman (1960) and early developments in the subject took place in the field of engineering. Once in state space form, the Kalman filter may be applied and this in turn leads to estimation, analysis and forecasting. The state space model in its simple form can be expressed as y Z a, a t t t T a R, t ~ N(0, H ), ~ N(0, Q ), t 1 t t t t t t with initial value a1 ~ N( 1, P1 ) t t where matrices be relaxed). Z, t, Ht, Tt Rt and t Q are assumed known (however this assumption can Note matrices equations and H t and Z T, R t t t Q t are covariance matrices associated with the errors of the, are matrices used to appropriately define a multitude of models and they may contain coefficients to be estimated as well. 1 Disaggregate models, providing detailed insights, require more detailed data. At subgroup level, exposure data are difficult to find and often nonexisting on a e.g. monthly basis. Steunpunt Mobiliteit & Openbare Werken 8 RAMOW
9 The key idea of state space models is that a certain parameter a t relates to the parameter at the previous time point, inducing a dynamic linear model. The first equation is called the observation (or measurement) equation and the second equation is called the state equation. The state space formulation for time series models is quite general and encompasses most of the classical time series models like MA and ARIMA models for example. Also since the state equation(s) can capture in a very flexible way the behaviour of the underlying (and unobservable) variables it offers great flexibility with real data. The advantages of state space modelling can be summarized (see, e.g. Durbin and Koopman, 2001) as: They are based on a structural analysis of the problem at hand. The different components that may comprise a time series model, can themselves be modelled separately. They offer greater generality. In fact, several other models can be seen as special case of the state space models. They satisfy the Markovian property and hence the necessary calculations can be put in a typical recursive manner. Forecasting with state space models is relatively easy and simple. State space models in fact apply some smoothing in the data and hence forecasts are also smooth. In addition, diagnostic checking is simple as the Kalman filter employed provide such a framework. State space models are adaptive and the benefits of this are usually realised by implementing them in real time since only minor calculations are needed. Finally, they offer great flexibility as they can be used in certain circumstances, allowing for refined modelling in several problems. At the same time, some disadvantages should be mentioned. The models are usually more complicated and less interpretable than standard time series models, especially for nontreated researchers making their acceptance in some problems not easy. In addition, some added computational effort is needed with respect to much simpler models. Finally, note that while for certain models state space modelling is well established and easy to use, there are models where it is not so easy, like for example discrete valued time series models. The model developed by Zeger (1988) is in fact a statespace model for modelling discrete time series. However, assuming a Poisson distribution leads to a rather complicated recursion for the state equation and makes estimation difficult. State space models are currently popular models for accident prediction mainly due to their generality and flexibility (see e.g. Gould et al, 2004, Hermans et al, 2006a, 2006b, Bijleveld, 2008). Several software packages (like R, EVIEWS, MATLAB just to name a few) are available for fitting such models (see the special issue of Journal of Statistical Software, Commandeur et al, 2011). State space models provide a convenient Steunpunt Mobiliteit & Openbare Werken 9 RAMOW
10 and powerful framework for analyzing time series data. More details can be found in several textbooks devoted to these models, see e.g. Durbin and Koopman (2001) and Commandeur and Koopman (2007). Steunpunt Mobiliteit & Openbare Werken 10 RAMOW
11 3. T H E L A T E N T R I S K M O D E L The Latent Risk Time series Model (LRT) was introduced by Bijleveld et al. (2008). The LRT model is a particular case of statespace models. It has been developed in order to capture the idea of risk in road safety, an unobservable quantity which in fact plays a very important role in accident analysis. Road safety is usually affected by two factors: the risk and the exposure of the individuals to that risk. This approach was first developed by Oppe (1989, 1991). This decomposition implies that in order to analyze issues related to road safety one must be able to measure both quantities. While exposure can be measured using several different indicators, measurement of the risk is not easy. The cornerstone assumption is that traffic safety is the product of the respective developments of exposure and risk (Bijleveld, 2008); typically, exposure can be measured by traffic volume while number of fatalities (or casualties in general) is the product of exposure and (fatal) risk (which is unobservable). The stochastic model considered implies also some errors added to the above relationships, i.e. traffic volume is a proxy of exposure and not a full observation of it while the product of exposure and risk does not fully determines the fatalities. Typically one works with logarithms. A plain explanation for this is that firstly road safety quantities are positive numbers so logarithmic transformation guarantees consistent estimation. Secondly, taking logarithms implies a linear relationship in the logarithmic scale which is a more realistic assumption and thirdly, this makes the developed models easier to be fitted with real data. The LRT model developed in Bijleveld (2008) contains two measurement equations: one for traffic volume, and one for fatalities. In fact the model simultaneously fits two dependent variables (traffic volume and fatalities). In addition to each of these measurement equations two state equations correspond: For traffic volume the measurement equation is (3.1) while the state equations are (3.2) For the fatalities, the measurement equation is: while the state equations are: R (3.3) R (3.4) where is the traffic volume at time t, is the exposure variable at time t, is the number of fatalities at time t, and is the risk at time t, which is not observed, i.e. it is latent. Several extensions of this basic model can be considered, by allowing additional explanatory variables to be present, including the case of dummy variables, usually with Steunpunt Mobiliteit & Openbare Werken 11 RAMOW
12 respect to interventions. Also, note that in the models above we assume normal distributions for the errors considered. This allows to create models inside the normal family. Estimation is not straightforward due to the recursive way in which the model is defined. Kalman filters are of special importance for such models. The LRT allows to consider together all the important aspects of road safety. Risk is latent and quantified via this model. The errors are considered to be normally distributed, which implies that the two dependent variables are normally distributed in the logarithmic scale. For details about estimation, prediction and other statistical properties we refer the interested reader to Bijleveld et al. (2008). Steunpunt Mobiliteit & Openbare Werken 12 RAMOW
13 4. R E S U L T S F R O M A G G R E G A T E M O D E L S In this section we present the results on the aggregate forecasts for Flanders. Annual observed data from 1991 to 2007 were used. The road safety indicators considered are the number of fatalities, the total number of casualties, the number of severely injured persons and the number of slightly injured persons. The official Flemish casualty data was obtained from the FOD Economie. With respect to exposure we used the number of total kilometres travelled for that period in millions (Federaal Planbureau). The LRT model described in section 3 was fitted, thereby jointly modelling exposure on the one hand and a road safety outcome indicator (e.g. fatalities) on the other. Note that we have used the same model for casualties and injuries since the idea of risk is the same for these kinds of measurements of traffic safety. Figure 1 presents the real data and the forecasted values. The vertical dotted line implies the period where the forecasting started (2008 in this study). On the left, the observed values are shown while on the right we can see the forecasted values (dots) based on the model, together with a 95% forecasting interval to present the uncertainty around the forecast. The fitted model implied a linear forecasting, but this applies to the logarithmic scale as described in section 3, so we can see some curvature in the predictions. Figure 1 Forecasted values for the years together with the observed values for the number of fatalities in Flanders (yearly data available for ) Steunpunt Mobiliteit & Openbare Werken 13 RAMOW
14 From Figure 1 a downward trend in the number of fatalities can be deduced resulting in a forecasted number of 360 fatalities by As expected a longer forecasting horizon implies a larger uncertainty. Note that the interpretation of the figure is the same for all figures presented in this section. Figure 2 deals with the number of casualties (i.e. the sum of fatalities, severe injuries and slight injuries). The uncertainty is much larger now. There is again a downward trend yet it is less than the one for fatalities. An explanation for this is that (see the right panel in Figure 3) the slight injuries are not expected to decrease a lot and they make up a larger part of the casualties. Figure 2 Forecasted values for the years together with the observed values for the total number of casualties in Flanders (yearly data available for ). Figure 3 presents the forecasts for the severe injuries (left) respectively slight injuries (right). Severe injuries are forecasted to decrease to 3690 by For slight injuries one notices that the variability is very large and that the overall trend in is not decreasing but rather stable (keeping in mind the large fluctuations that were present). Thus forecasted values are quite close to the 2007 level and not expected to decrease a lot. As already mentioned this has an effect on the overall number of casualties as slight injuries are the largest contributor to this number. Steunpunt Mobiliteit & Openbare Werken 14 RAMOW
15 Figure 3 Forecasted values for the years together with the observed values for the number of severely injured persons on the left panel and slightly injured persons on the right panel, in Flanders (yearly data available for ). Table 1 summarizes the forecasts for all four measurements of traffic safety. Regarding casualties we present two forecasts: one when using the aggregated data (column (1)) and one when each component is forecasted separately and then summed to obtain a forecast for casualties (column (5)). The differences are rather small, as the maximum proportion is around 1% for 2015, which implies a rather good correspondence between both forecasts. The forecasts in column (1) are shown in Figure 2 as they allow for a better estimation of the standard erros. To conclude, table 1 clearly shows that all road safety outcomes are expected to decrease in the following years. Year Casualties (1) Fatalities (2) Severe Injuries (3) Slight Injuries (4) Prediction of casualties from separate components (5)= (2)+(3)+(4) (5)(1) Relative difference % % % % % % % % Table 1. Forecasts for the different road safety outcomes. Column (5) predicts the casualties as the sum of the forecasted number of fatalities, severe and slight injuries. The difference from the direct forecast is negligible. Steunpunt Mobiliteit & Openbare Werken 15 RAMOW
16 As far as exposure is concerned, all 4 models provided forecasts for the total kilometres travelled (in millions). They are presented in Figure 4. In the Appendix (A2) we also depict the uncertainty around the 4 forecasts which clearly shows that the forecasts mostly agree and the observed small differences are due to the model and the uncertainty of the different variables used in the LRT model. Similar analyses are reported for other variables in Appendices A3 and A4. The values of the forecasts can be read from Table 2 for all the models. Figure 4. Forecasted values about exposure (in million of total kms travelled) for the years together with the observed values in Flanders (yearly data available for ). The four predictions are based on the traffic safety variable used in the LRT model. Forecast based on Severe Injuries Slight Injuries Year Casualties Fatalities Table 2. Forecasts for the exposure variable, i.e. the total kilometres travelled in millions. We obtained 4 forecasts, one from each model depending on the traffic safety variable used. Steunpunt Mobiliteit & Openbare Werken 16 RAMOW
17 Summarizing so far, the fatalities are forecasted to be reduced to 360 by Also the severe injuries are expected to decrease but for slight injuries the decrease is expected to be very small. Since we use data up to to predict the period from 2008 up to 2015, the data for (which became available in the meantime) can be used to comment on the prediction accuracy of the developed model. The comparison is shown in Table 3. Slight injuries Severe injuries Fatalities Observed Forecasted Observed Forecasted Observed Forecasted Table 3. Forecasts based on the developed model and the real observed values for slight injuries, severe injuries and fatalities ( ). One can see that while for 2008 the forecasted values are close to the real figures, as time passes the forecasts are less accurate. Recall that in almost all the cases the values are within the 95% forecast intervals, i.e. taking into account the uncertainty the model does not fail to forecast. However, as time passes, the observed values are closer to the lower limit of the forecasted intervals. Slight injuries are forecasted worse by the model, while fatalities are forecasted more reasonably. About the target of maximum 250 fatalities and maximum 2000 severely injured persons by the year 2015, this seem not to be validated by the model. Concerning the number of fatalities, 250 is still inside the forecasting 95% interval but very close to the boundary, while for the number of severe injuries, the value of 2000 is outside. Hence, the model shows that the targets are hard to be met by In Appendix A1 a small comparison with other simpler models is shown. The findings are similar was the most recent year for which detailed data was available at the start of the analyses. Steunpunt Mobiliteit & Openbare Werken 17 RAMOW
18 5. R E S U L T S F R O M D I S A G G R E G A T E M O D E L S Disaggregate models are tools for assessing different policy options, setting goals for safety programmes and predicting future safety developments at the disaggregated level. This makes their development of particular importance for better understanding the problem but also for policy and decision making purposes. While disaggregate models can suffer from lack of data, in our case quite accurate and detailed data for certain subcategories exist and thus we present such an analysis in this section. We primarily focus on the fatalities as for this variable the data are more accurate and detailed. However, in Appendix A5 the results from the disaggregated analyses using the (larger) number of casualties are presented. Note that there are two issues that tend to limit the scope for disaggregation. The first one refers to the fact that the numbers (of e.g. fatalities) in each group are typically much less than the overall number (of fatalities), which leads to increased variability. Consequently, it is more difficult to identify trends and hence the uncertainty on predictions is larger. This implies limitations on the level of disaggregation that can be used. The second issue relates to the availability of exposure measures which may be available for the whole population but not for each group separately. In this section, we present the results of applying the LRT model focusing on the following subgroups: Age classes split in 4 categories (ages 018, 1945, 4664, 65+). Type of road user (cars, trucks, small vans and motorcycles). Type of road (motorways and nonmotorways). We have fitted a separate LRT model to each subgroup. Details follow when describing each subgroup. 5.1 By Road user type We worked with 4 categories of road user namely cars, small vans, motorcycles, and trucks. There were data available for other categories like buses but the number of fatalities were too small to build any interesting model. Recall that in the disaggregate analyses we primarily focus on the fatalities as we aim to identify the subgroups with a large share in the forecasted number of fatalities or with a high (or even increased) fatal risk in the future. As exposure variable for the 4 categories we used data on the number of kilometres travelled by this mode (Federaal Planbureau). Road user types for which no (good) exposure data was available (such as pedestrians) were not considered for analysis. Results from the model, with respect to fatalities are reported in Figure 5. One can notice the wide confidence intervals, implying that the uncertainty around the forecasts is large, perhaps invalidating the forecasts themselves. The overall trend is decreasing. There is a clear downwards trend for cars, motorcycles, trucks and a smaller one for small vans. However, the large uncertainty Steunpunt Mobiliteit & Openbare Werken 18 RAMOW
19 prohibits deriving clear conclusions for all the road user types and thus any result should be interpreted with care. Note also that the data availability covered a smaller time period than the aggregate data, namely only from 1997 to Figure 5: Forecasted fatalities and corresponding 95% intervals for different road user types. The available data cover the period Table 4 contains the forecasted values. The last column is the sum of the values for the 4 user types which is smaller than the number of fatalities forecasted in section 4 since we miss data for some accidents (covering an inhomogeneous class named other which is not used in the forecasting) but also some road users were excluded due to nonavailability of reliable exposure data. Steunpunt Mobiliteit & Openbare Werken 19 RAMOW
20 Year car Road user small van motorcycle truck Total 2007 (observed) Change from % % % % % % % % Proportion of each user type to the total % 7.74% 16.33% 3.44% % 6.52% 25.76% 1.85% Table 4. Forecasts for the number of fatalities for different types of road users. Forecasts are derived from the LRT model covering the period The last column presents the percentage of decrease from The models forecast a large decrease up to 50% for the year Also at the bottom of the table we have calculated the share of each of the four considered road user types to the total. Interestingly while the car fatalities will decrease, a large increase on the fatalities in motorcycles is expected (from 16.3% in 2007 up to 25.8% in 2015). Also note that the overall decrease concerning motorcycle fatalities is the smallest. Finally, forecasts for the traffic volumes can be read from Table 5 (in millions of vehicle kilometres). The general trend is increasing for all modes. It is interesting however to note that after a small decrease, the model forecasts an increase which is up to 4.8% for 2015 (compared to 2007). The corresponding graphs can be found in Appendix A3. Year Car Road user small van motorcycle truck Total Change since % % % % % % % % Table 5 Forecasts for the traffic volumes for 4 categories of road user type. Steunpunt Mobiliteit & Openbare Werken 20 RAMOW
Impact of Methodological Choices on Road Safety Ranking
Impact of Methodological Choices on Road Safety Ranking RAMOW2007001 Elke Hermans, Filip Van den Bossche, Geert Wets Onderzoekslijn Risicobepaling DIEPENBEEK, 2012. STEUNPUNT MOBILITEIT & OPENBARE WERKEN
More informationCycling more for safer cycling
Cycling more for safer cycling Cycling presents a lot of benefits to the individual and to society. Health, environment, accessibility, local businesses, all gain when more people cycle. Yet many governments
More informationIntroduction to time series analysis
Introduction to time series analysis Margherita Gerolimetto November 3, 2010 1 What is a time series? A time series is a collection of observations ordered following a parameter that for us is time. Examples
More informationCURRICULUM VITAE. Tim De Ceunynck
CURRICULUM VITAE Tim De Ceunynck Campus Diepenbeek Transportation Research Institute (IMOB) Wetenschapspark 5 bus 6 BE3590 Diepenbeek Belgium Tel: +32 (0)11 26 91 18 Fax: +32 (0)11 26 91 99 Email: tim.deceunynck@uhasselt.be
More information7 Time series analysis
7 Time series analysis In Chapters 16, 17, 33 36 in Zuur, Ieno and Smith (2007), various time series techniques are discussed. Applying these methods in Brodgar is straightforward, and most choices are
More informationAnalysis of Bayesian Dynamic Linear Models
Analysis of Bayesian Dynamic Linear Models Emily M. Casleton December 17, 2010 1 Introduction The main purpose of this project is to explore the Bayesian analysis of Dynamic Linear Models (DLMs). The main
More informationStatistical Forecasting of HighWay Traffic Jam at a Bottleneck
Metodološki zvezki, Vol. 9, No. 1, 2012, 8193 Statistical Forecasting of HighWay Traffic Jam at a Bottleneck Igor Grabec and Franc Švegl 1 Abstract Maintenance works on highways usually require installation
More informationReported Road Casualties Great Britain: 2013 Annual Report
Reported Road Casualties Great Britain: 2013 Annual Report Focus on pedal cyclists Key findings The key findings from this article include: Pedal cyclist deaths have seen a longterm fall, but have fluctuated
More informationPractical Time Series Analysis Using SAS
Practical Time Series Analysis Using SAS Anders Milhøj Contents Preface... vii Part 1: Time Series as a Subject for Analysis... 1 Chapter 1 Time Series Data... 3 1.1 Time Series Questions... 3 1.2 Types
More informationOxfordshire Local Transport Plan 20112030 Revised April 2012. Objective 3 Reduce casualties and the dangers associated with travel
6. Road Safety Objective 3 Reduce casualties and the dangers associated with travel Road safety continues to be a core priority both nationally and locally reflecting the very high human and other costs
More informationStatistics in Retail Finance. Chapter 6: Behavioural models
Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics: Behavioural
More informationNEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS
NEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS 168 MOTOR VEHICLE CRASHES IN NEW ZEALAND 213 CONTENTS TABLES Table 1 New Zealand Injury Prevention Strategy serious outcome indicators
More informationPREDICTING THE USED CAR SAFETY RATINGS CRASHWORTHINESS RATING FROM ANCAP SCORES
PREDICTING THE USED CAR SAFETY RATINGS CRASHWORTHINESS RATING FROM ANCAP SCORES by Stuart Newstead and Jim Scully April 2012 Report No. 309 Project Sponsored By The Vehicle Safety Research Group ii MONASH
More informationState Space Time Series Analysis
State Space Time Series Analysis p. 1 State Space Time Series Analysis Siem Jan Koopman http://staff.feweb.vu.nl/koopman Department of Econometrics VU University Amsterdam Tinbergen Institute 2011 State
More informationIRGRail (13) 2. Independent Regulators Group Rail IRG Rail Annual Market Monitoring Report
IRGRail (13) 2 Independent Regulators Group Rail IRG Rail Annual Market Monitoring Report February 2013 Index 1 Introduction...3 2 Aim of the report...3 3 Methodology...4 4 Findings...5 a) Market structure...5
More informationNEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS
NEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS 166 MOTOR VEHICLE CRASHES IN NEW ZEALAND 2012 CONTENTS TABLES Table 1 New Zealand Injury Prevention Strategy serious outcome indicators
More information16 : Demand Forecasting
16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical
More informationMarketing Mix Modelling and Big Data P. M Cain
1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored
More informationCalculating Interval Forecasts
Calculating Chapter 7 (Chatfield) Monika Turyna & Thomas Hrdina Department of Economics, University of Vienna Summer Term 2009 Terminology An interval forecast consists of an upper and a lower limit between
More informationAccident configurations and injuries for bicyclists based on the German InDepth Accident Study. Chiara Orsi
Accident configurations and injuries for bicyclists based on the German InDepth Accident Study Chiara Orsi Centre of Study and Research on Road Safety University of Pavia State of the art Vulnerable road
More informationMemorial Day Holiday Period Traffic Fatality Estimate, 2015
Memorial Day Holiday Period Traffic Fatality Estimate, 2015 Prepared by Research & Statistics Department National Safety Council May 8, 2015 Holiday period definition Memorial Day is May 30 but it is observed
More informationPenalized regression: Introduction
Penalized regression: Introduction Patrick Breheny August 30 Patrick Breheny BST 764: Applied Statistical Modeling 1/19 Maximum likelihood Much of 20thcentury statistics dealt with maximum likelihood
More informationELASTICITY OF LONG DISTANCE TRAVELLING
Mette Aagaard Knudsen, DTU Transport, mak@transport.dtu.dk ELASTICITY OF LONG DISTANCE TRAVELLING ABSTRACT With data from the Danish expenditure survey for 12 years 1996 through 2007, this study analyses
More informationQuantifying the Influence of Social Characteristics on Accident and Injuries Risk: A Comparative Study Between Motorcyclists and Car Drivers
Downloaded from orbit.dtu.dk on: Dec 15, 2015 Quantifying the Influence of Social Characteristics on Accident and Injuries Risk: A Comparative Study Between Motorcyclists and Car Drivers Lyckegaard, Allan;
More informationLife Cycle Cost Analysis (LCCA)
v011911 Life Cycle Cost Analysis (LCCA) Introduction The SHRP2 R23 Guidelines provide a number of possible alternative designs using either rigid of flexible pavements. There is usually not a single
More informationTraffic Accident Trends in Hong Kong
Traffic Accident Trends in Hong Kong Traffic Accident Trends in Hong Kong INTRODUCTION 4.1 With a total area of 1 102 km 2, Hong Kong has a population of 6.8 million and 522 912 licensed vehicles as at
More informationAuxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationSection A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA  Part I
Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA  Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting
More informationTime Series Analysis
Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina GarcíaMartos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and GarcíaMartos
More informationEast Ayrshire Council Road Safety Plan
East Ayrshire Council Road Safety Plan Foreword Road crashes are not inevitable  the deaths and injuries which occur each year need not happen. However, in order to reduce these incidents a major effort
More information1 Example of Time Series Analysis by SSA 1
1 Example of Time Series Analysis by SSA 1 Let us illustrate the 'Caterpillar'SSA technique [1] by the example of time series analysis. Consider the time series FORT (monthly volumes of fortied wine sales
More informationSummary. How severe are the injuries of victims of road traffic accidents
Summary How severe are the injuries of victims of road traffic accidents Analysis of the MAIS severity scale for injuries suffered by victims of road traffic accidents hospitalized in Belgian hospitals
More informationMISSOURI TRAFFIC SAFETY COMPENDIUM
2010 MISSOURI TRAFFIC SAFETY COMPENDIUM MISSOURI STATE HIGHWAY PATROL STATISTICAL ANALYSIS CENTER 1510 East Elm Jefferson City, Missouri 65101 (573) 7519000 CONTENTS PAGE EXECUTIVE SUMMARY INTRODUCTION...1
More informationA Regional Demand Forecasting Study for Transportation Fuels in Turkey
A al Demand Forecasting Study for Transportation Fuels in Turkey by Özlem Atalay a, Gürkan Kumbaroğlu Bogazici University, Department of Industrial Engineering, 34342, Bebek, Istanbul, Turkey, Phone :
More informationImplementations of tests on the exogeneity of selected. variables and their Performance in practice ACADEMISCH PROEFSCHRIFT
Implementations of tests on the exogeneity of selected variables and their Performance in practice ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag
More informationReported Road Accident Statistics
Reported Road Accident Statistics Standard Note: SN/SG/2198 Last updated: 24 October 2013 Author: Matthew Keep & Tom Rutherford Social and General Statistics Section This Note provides a range of data
More informationTHE COST OF ROAD TRAFFIC ACCIDENT IN VIETNAM
THE COST OF ROAD TRAFFIC ACCIDENT IN VIETNAM Trinh Thuy ANH Lecturer. University of Transport and Communication. Department of Transport  Economics Address: Caugiay, Ha Noi, Vietnam Tel: 84 4 8674702
More informationthe Ministry of Transport is attributed as the source of the material
Disclaimer All reasonable endeavours are made to ensure the accuracy of the information in this report. However, the information is provided without warranties of any kind including accuracy, completeness,
More informationHandling attrition and nonresponse in longitudinal data
Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 6372 Handling attrition and nonresponse in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein
More informationChapter 27 Using Predictor Variables. Chapter Table of Contents
Chapter 27 Using Predictor Variables Chapter Table of Contents LINEAR TREND...1329 TIME TREND CURVES...1330 REGRESSORS...1332 ADJUSTMENTS...1334 DYNAMIC REGRESSOR...1335 INTERVENTIONS...1339 TheInterventionSpecificationWindow...1339
More informationThe effects of Michigan s weakened motorcycle helmet use law on insurance losses
Bulletin Vol. 30, No. 9 : April 2013 The effects of Michigan s weakened motorcycle helmet use law on insurance losses In April of 2012 the state of Michigan changed its motorcycle helmet law. The change
More informationTime valuation in traffic
Time valuation in traffic Congestion costs, value of time & lost vehicle hours. RAMOW2011001 K. Van Raemdonck, C. Macharis Onderzoekslijn Evaluatietechnieken DIEPENBEEK, 2010. STEUNPUNT MOBILITEIT &
More informationMotorcyclists killed and injured (1980 2012)
Motorcyclists CRASH FACTSHEET November 2013 CRASH STATISTICS FOR THE YEAR ENDED 31 DECEMBER 2012 Prepared by the Ministry of Transport In 2012, 50 motorcyclists 1 died and a further 1,138 were injured
More informationATSB RESEARCH AND ANALYSIS REPORT ROAD SAFETY. Characteristics of Fatal Road Crashes During National Holiday Periods
ATSB RESEARCH AND ANALYSIS REPORT ROAD SAFETY Characteristics of Fatal Road Crashes During National Holiday Periods July 2006 ATSB RESEARCH AND ANALYSIS REPORT ROAD SAFETY Characteristics of Fatal Road
More informationPolicy Document Road safety
Policy Document Road safety Type hier de hoofdstuk as one titel Road safety: working together The number of road deaths in the Netherlands has been steadily decreasing since the 1970s. This number rose
More informationIntegrated Resource Plan
Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 6509629670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1
More informationCharacteristics of High Injury Severity Crashes on 80 110 km/h Rural Roads in South Australia
Characteristics of High Injury Crashes on 80 110 km/h Rural Roads in South Australia, J. R. R. Centre for Automotive Safety Research, University of Adelaide, SOUTH AUSTRALIA, 5005 email: jamie@casr.adelaide.edu.au
More informationA Short review of steel demand forecasting methods
A Short review of steel demand forecasting methods Fujio John M. Tanaka This paper undertakes the present and past review of steel demand forecasting to study what methods should be used in any future
More informationInequality, Mobility and Income Distribution Comparisons
Fiscal Studies (1997) vol. 18, no. 3, pp. 93 30 Inequality, Mobility and Income Distribution Comparisons JOHN CREEDY * Abstract his paper examines the relationship between the crosssectional and lifetime
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationDeaths/injuries in motor vehicle crashes per million hours spent travelling, July 2008 June 2012 (All ages) Mode of travel
Cyclists CRASH STATISTICS FOR THE YEAR ENDED 31 DECEMBER 212 Prepared by the Ministry of Transport CRASH FACTSHEET November 213 Cyclists have a number of risk factors that do not affect car drivers. The
More informationOrganizing Your Approach to a Data Analysis
Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize
More informationAssociation Between Variables
Contents 11 Association Between Variables 767 11.1 Introduction............................ 767 11.1.1 Measure of Association................. 768 11.1.2 Chapter Summary.................... 769 11.2 Chi
More informationCOMBINING THE METHODS OF FORECASTING AND DECISIONMAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES
COMBINING THE METHODS OF FORECASTING AND DECISIONMAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES JULIA IGOREVNA LARIONOVA 1 ANNA NIKOLAEVNA TIKHOMIROVA 2 1, 2 The National Nuclear Research
More informationUnivariate and Multivariate Methods PEARSON. Addison Wesley
Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston
More informationAnalysis of trends over time for motorcycle crashes in South Australia
Analysis of trends over time for motorcycle crashes in South Australia Adrian Weissenfeld Dr Matthew Baldock Dr Paul Hutchinson Centre for Automotive Safety Research, University of Adelaide Email: adrian@casr.adelaide.edu.au
More information5 TRAFFIC ACCIDENT COSTS IN THAILAND
5 TRAFFIC ACCIDENT COST IN THAILAND 51 5 TRAFFIC ACCIDENT COSTS IN THAILAND 5.1 Introduction Investigation into the range of costs incurred by road crashes in the five pilot provinces, as discussed in
More informationLife Cycle Asset Allocation A Suitable Approach for Defined Contribution Pension Plans
Life Cycle Asset Allocation A Suitable Approach for Defined Contribution Pension Plans Challenges for defined contribution plans While Eastern Europe is a prominent example of the importance of defined
More informationFairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
More informationConcept Design. Gert Landheer Mark van den Brink Koen van Boerdonk
Concept Design Gert Landheer Mark van den Brink Koen van Boerdonk Content Richness of Data Concept Design Fast creation of rich data which eventually can be used to create a final model Creo Product Family
More informationESSAYS ON MONTE CARLO METHODS FOR STATE SPACE MODELS
VRIJE UNIVERSITEIT ESSAYS ON MONTE CARLO METHODS FOR STATE SPACE MODELS ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus
More informationFourwheel drive vehicle crash involvement patterns
Fourwheel drive vehicle crash involvement patterns August 2006 Report Summary 06/05 Introduction This document is a summary of a larger research report prepared by the Monash University Accident Research
More information4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4
4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Nonlinear functional forms Regression
More informationAn introduction to ValueatRisk Learning Curve September 2003
An introduction to ValueatRisk Learning Curve September 2003 ValueatRisk The introduction of ValueatRisk (VaR) as an accepted methodology for quantifying market risk is part of the evolution of risk
More informationRoad safety Performance Indicators in Hungary
Acta Technica Jaurinensis Vol. 3. No. 1. 2010 Road safety Performance Indicators in Hungary P. Holló KTI Institute for Transport Sciences Nonprofit Ltd. H1518 Budapest, PO Box 107, Hungary Phone : +36(1)3715823,
More informationDECISION TREE ANALYSIS: PREDICTION OF SERIOUS TRAFFIC OFFENDING
DECISION TREE ANALYSIS: PREDICTION OF SERIOUS TRAFFIC OFFENDING ABSTRACT The objective was to predict whether an offender would commit a traffic offence involving death, using decision tree analysis. Four
More informationIndependence Day 2016 Holiday Period Traffic Fatality Estimate
Independence Day 2016 Holiday Period Traffic Fatality Estimate The 2016 Independence Day holiday period begins at 6:00 p.m., Friday, July 1, and ends at 11:59 p.m., Monday, July 4. Our estimate of traffic
More informationHow valid are Motorcycle Safety Data?
How valid are Motorcycle Safety Data? Narelle Haworth 1 (Presenter) 1 Monash University Accident Research Centre Biography Dr Narelle Haworth began working at the Monash University Accident Research Centre
More informationAdvanced Forecasting Techniques and Models: ARIMA
Advanced Forecasting Techniques and Models: ARIMA Short Examples Series using Risk Simulator For more information please visit: www.realoptionsvaluation.com or contact us at: admin@realoptionsvaluation.com
More informationTraffic accidents in Hanoi: data collection and analysis
Traffic accidents in Hanoi: data collection and analysis Nguyen Hoang Hai Vietnam, Hanoi Department of Transport, haitups@yahoo.com.au 1. Introduction Hanoi, the capital and administrative center of Vietnam,
More informationMOTORBIKE RIDERS AND CYCLISTS
HSRC MOTORBIKE RIDERS AND CYCLISTS 113 8 MOTORBIKE RIDERS AND CYCLISTS 8.1 INTRODUCTION Motorbike and bicycle riders constitute only a small portion of road accident victims. In the RAF system only 1%
More informationChapter 17: Aggregation
Chapter 17: Aggregation 17.1: Introduction This is a technical chapter in the sense that we need the results contained in it for future work. It contains very little new economics and perhaps contains
More informationThe primary goal of this thesis was to understand how the spatial dependence of
5 General discussion 5.1 Introduction The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial
More informationKilled 2013 upper estimate Killed 2013 lower estimate Killed 2013 central estimate 700
Statistical Release 12 February 2015 Estimates for reported road traffic accidents involving illegal alcohol levels: 2013 (second provisional) Selfreported drink and drug driving for 2013/14 Main findings
More informationEconometric analysis of the Belgian car market
Econometric analysis of the Belgian car market By: Prof. dr. D. Czarnitzki/ Ms. Céline Arts Tim Verheyden Introduction In contrast to typical examples from microeconomics textbooks on homogeneous goods
More informationComposite performance measures in the public sector Rowena Jacobs, Maria Goddard and Peter C. Smith
Policy Discussion Briefing January 27 Composite performance measures in the public sector Rowena Jacobs, Maria Goddard and Peter C. Smith Introduction It is rare to open a newspaper or read a government
More informationSUMMARY OF THE IMPACT ASSESSMENT
EN EN EN COMMISSION OF THE EUROPEAN COMMUNITIES Brussels, SEC(2008) 350/2 COMMISSION STAFF WORKING DOCUMENT accompanying the Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL facilitating
More informationReview of 20 mph zones in London Boroughs. by D C Webster and R E Layfield. Published Project Report PPR243
Review of 20 mph zones in London Boroughs by D C Webster and R E Layfield Published Project Report PPR243 Review of 20 mph zones in London Boroughs by D C Webster and R E Layfield PUBLISHED PROJECT REPORT
More informationECONOMETRIC THEORY. MODULE I Lecture  1 Introduction to Econometrics
ECONOMETRIC THEORY MODULE I Lecture  1 Introduction to Econometrics Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Econometrics deals with the measurement
More informationEstimating and Forecasting Network Traffic Performance based on Statistical Patterns Observed in SNMP data.
Estimating and Forecasting Network Traffic Performance based on Statistical Patterns Observed in SNMP data. K. Hu 1,2, A. Sim 1, Demetris Antoniades 3, Constantine Dovrolis 3 1 Lawrence Berkeley National
More informationDOT HS 811 737 Summary of Statistical Findings April 2013. Methodology
TRAFFIC SAFETY FACTS Research Note DOT HS 811 737 Summary of Statistical Findings April 2013 Distracted Driving 2011 Distracted driving is a behavior dangerous to, passengers, and nonoccupants alike. Distraction
More informationWhat Can We Learn by Disaggregating the UnemploymentVacancy Relationship?
What Can We Learn by Disaggregating the UnemploymentVacancy Relationship? No. 1 Rand Ghayad and William Dickens Abstract: The Beveridge curve the empirical relationship between unemployment and job vacancies
More informationChapter 25 Specifying Forecasting Models
Chapter 25 Specifying Forecasting Models Chapter Table of Contents SERIES DIAGNOSTICS...1281 MODELS TO FIT WINDOW...1283 AUTOMATIC MODEL SELECTION...1285 SMOOTHING MODEL SPECIFICATION WINDOW...1287 ARIMA
More informationC: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)}
C: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)} 1. EES 800: Econometrics I Simple linear regression and correlation analysis. Specification and estimation of a regression model. Interpretation of regression
More informationAdvanced timeseries analysis
UCL DEPARTMENT OF SECURITY AND CRIME SCIENCE Advanced timeseries analysis Lisa Tompson Research Associate UCL Jill Dando Institute of Crime Science l.tompson@ucl.ac.uk Overview Fundamental principles
More informationEXPOSURE WORK COMMUTING: CASE STUDY AMONG COMMUTING ACCIDENT IN KLANG VALLEY, MALAYSIA
EXPOSURE WORK COMMUTING: CASE STUDY AMONG COMMUTING ACCIDENT IN KLANG VALLEY, MALAYSIA Nurulhuda JAMALUDDIN, HO Jen Sim, Akmalia SHABADIN, Nusayba MJ and Wahida AB Road Safety Engineering and Environment
More informationIntegrating Financial Statement Modeling and Sales Forecasting
Integrating Financial Statement Modeling and Sales Forecasting John T. Cuddington, Colorado School of Mines Irina Khindanova, University of Denver ABSTRACT This paper shows how to integrate financial statement
More informationRegression III: Advanced Methods
Lecture 4: Transformations Regression III: Advanced Methods William G. Jacoby Michigan State University Goals of the lecture The Ladder of Roots and Powers Changing the shape of distributions Transforming
More informationInjury indicators: A validation tool. Road safety indicator specifications
Injury indicators: A validation tool. Road safety indicator specifications Colin Cryer CHSS, University of Kent March 2002. The following gives the specifications of the indicators used in the project:
More informationDEDICATION. To my parents, wife, daughter, brothers and sisters. for all their patience, understanding and support
ii iii DEDICATION To my parents, wife, daughter, brothers and sisters for all their patience, understanding and support iv AKNOWLEDGMENT I am sincerely greatful to prof. Adli AlBalbissi for his advice,
More informationThe number of fatalities fell even further last year to below 6,000 for the first time in 54 years since 1953.
1 Longterm trends The number of fatalities fell even further last year to below 6,000 for the first time in 54 years since 1953. Number of road traffic accidents, fatalities, and injuries Notes: 1. Source:
More informationTime Series Analysis. 1) smoothing/trend assessment
Time Series Analysis This (not surprisingly) concerns the analysis of data collected over time... weekly values, monthly values, quarterly values, yearly values, etc. Usually the intent is to discern whether
More informationMotorcycle Safety Research in Belgium
Motorcycle Safety Research in Belgium Wouter Van den Berghe Research Director, Belgian Road Safety Institute August 2016 BRSI  Belgian Road Safety Institute Mission: to develop, share and apply knowlegde
More information# % # & ())! +,,, # ) (. / 0 1 ) / 2 3 4 ) )/)
! # % # & ())! +,,, # ) (. / 0 1 ) / 2 3 4 ) )/) 5 Hess & Polak 1 An Analysis of the Effects of Speed Limit Enforcement Cameras on Accident Rates Stephane Hess and John Polak Centre for Transport Studies
More informationTraffic Safety Facts Research Note
Traffic Safety Facts Research Note DOT HS 810 853 July 2008 Comparison of Crash Fatalities by Sex and Dow Chang Summary The purpose of this research note is to explore the ratio and distribution pattern
More information(More Practice With Trend Forecasts)
Stats for Strategy HOMEWORK 11 (Topic 11 Part 2) (revised Jan. 2016) DIRECTIONS/SUGGESTIONS You may conveniently write answers to Problems A and B within these directions. Some exercises include special
More information11.2 Monetary Policy and the Term Structure of Interest Rates
518 Chapter 11 INFLATION AND MONETARY POLICY Thus, the monetary policy that is consistent with a permanent drop in inflation is a sudden upward jump in the money supply, followed by low growth. And, in
More informationEffectiveness of Red Light Cameras in Tucson, AZ. PhotoTicketing.com. Ryan Denke, BSEE Peoria, AZ
Effectiveness of Red Light Cameras in Tucson, AZ PhotoTicketing.com Ryan Denke, BSEE Peoria, AZ Originally Published Oct 15, 215 Updated Oct 19, 215 INTRODUCTION The city of Tucson operates photo ticketing
More informationBefore and After Studies in Injury Research
Before and After Studies in Injury Research Thomas Songer, PhD University of Pittsburgh tjs@pitt.edu Before and After study designs are used very frequently in injury research. This lecture introduces
More informationA three dimensional stochastic Model for Claim Reserving
A three dimensional stochastic Model for Claim Reserving Magda Schiegl Haydnstr. 6, D  84088 Neufahrn, magda.schiegl@tonline.de and Cologne University of Applied Sciences Claudiusstr. 1, D50678 Köln
More information