the size of the coefficient. Furthermore we cannot explain the duration of the effects from a VAR analysis. (Brooks, 2008) To obtain this knowledge we will perform an impulse response function after the VAR analysis. The impulse response function will be helpful to retrieve the responsiveness of the dependent variables in the model to shocks to each of the variables. (Brooks, 2008) We will execute an impulse response analysis after the VAR analysis and present the results after the VAR output (sub. 4.1 and 4.2). It should however be kept in mind that it is hard to interpret the results of the impulse response accurately. (Runkle, 1987) A difficulty with time series variables is that cointegration can be of influence on the output of the VAR analysis making the results spurious. Time series variables can often be as individual variable but when variables move together over time, the variables are cointegrating. Cointegrating variables indicate that the time series variables are related in the long run caused by a certain influence on the time series. A cointegrating relationship may be seen as a long term equilibrium phenomenon, because in the short run the variables can differ from each other but in the long run their association becomes clear. (Brooks, 2008) When the variables are cointegrated, the VAR model is not sufficient and a vector error correction model (VEC model), based on the VAR model, is needed correct for the cointegration. (Adkins and Carter Hill, 2011) The VEC model differs from the VAR in the sense that is adds error correction features. More details on the VEC model will be provided in appendix C. The significance level (α) throughout the whole analysis is set at 5%. 3.3.1 Model I The first model makes use of the three components of vehicle crime namely vehicle, combination and cargo theft over the period January 1996 – December 2011. We base our hypotheses on the results generated by the correlation tables presented in paragraph 3.2.1 although those might be spurious. Based on the significantly positive correlations of the variables with their lags, each component of vehicle crime is expected to show a positive and significant relation with itself in the VAR and impulse response analysis. Vehicle theft and combination theft have shown a significant and positive correlation indicating they influence each other in a positive way. We expect to see this impact in the
Is freight exchange fraud an addition to vehicle crime? We will come to our conclusion by looking back on the obtained knowledge in each of the previous chapters. Liberalisation of EU freight transport lead to the opportunity for the freight exchange to enhance efficiency but also came along with a new form of crime, freight exchange fraud. The freight exchange and freight exchange fraud were examined based on conversations with experts and inspection of police reports. This research resulted in the following definition of freight exchange fraud: Freight exchange fraud is the deliberate misuse of the freight exchange with the aim to steal cargo to fund other activities of the criminal organization. According to our definition, the target of freight exchange fraud is cargo. Based on the definition, freight exchange fraud does not seem an addition to vehicle crime but an innovation in vehicle crime. Cargo criminals seem to have enlarged their scope by involving the freight exchange as addition source of knowledge. The different working methods of freight exchange fraud – false document companies, company take-overs and infiltrators – seem to be complex, but lucrative, ways to prepare cargo thefts. We have performed a variety of correlation analyses. The first two analyses revealed that the components of vehicle crime – cargo theft, vehicle theft and combination theft – significantly depend on each other. In addition to significant between-variable correlation, also significant, at the 1% significance level, within-variable correlation with all three lags is found for each of the vehicle crime variables. The significant within-variable correlation seems to suggest that actors are the same within a specific component of vehicle crime or that the circumstances did not change and that the criminals keep on performing a certain crime. The significant between-variable correlation might on the one hand plea for the same actors and on the other hand for different actors. From this result we might be able to conclude, although with great reluctance, that when actors are different, freight exchange fraud is expected to be an addition to vehicle crime. When the actors are the same, this would indicate that freight exchange fraud is perceived as an innovation to vehicle crime.
where ’ in which stands for vehicle theft, for combination theft and for the first difference of cargo theft. In the model a dummy is implemented to correct for the new measurement method which is implemented at the KLPD in 2010. The results of the VAR analysis can be found in table 7 and the results of the thereto belonging impulse response analysis can be found in appendix D.4.3. Table 7 VAR(4) analysis model I (cargo theft in first difference) Vector Auto Regression (4 lags) Observations: 187 Variable Vehicle theft D.Cargo theft Combination theft Vehicle L1 Standard error t-statistic 0.158 * 0.080 1.983 -0.179 0.115 -1.556 0.057 0.051 1.124 Vehicle L2 Standard error t-statistic 0.119 0.082 1.457 0.180 0.118 - 1.525 0.106 * 0.053 2.019 Vehicle L3 Standard error t-statistic 0.238 * 0.084 2.845 - 0.079 0.121 - 0.651 0.114 * 0.054 2.129 Vehicle L4 Standard error t-statistic 0.118 0.085 1.382 0.008 0.123 0.069 0.016 * 0.054 2.129 D.Cargo L1 Standard error t-statistic 0.039 0.053 0.736 - 0.657 * 0.076 - 8.587 0.014 0.034 0.414 D.Cargo L2 Standard error t-statistic 0.102 0.062 1.646 - 0.304 * 0.089 - 3.421 0.015 0.040 0.377 D.Cargo L3 Standard error t-statistic - 0.031 0.062 - 0.507 - 0.275 * 0.089 - 3.088 0.002 0.040 0.063 D.Cargo L4 Standard error t-statistic - 0.031 0.052 - 0.601 - 0.317 * 0.075 - 4.256 - 0.009 0.033 - 0.268