A Time-Series Study of the Formation and Predictive Performance of EEC Production Survey Expectations

Generalized method of moments (GMM) has been an important innovation in econometrics. Its usefulness has motivated a search for good inference procedures based on GMM. This article presents a novel method of bootstrapping for GMM based on resampling from the empirical likelihood distribution that imposes the moment restrictions. We show that this approach yields a large-sample improvement and is efficient, and give examples. We also discuss the development of GMM and other recent work on improved inference.     

Identifying traffic safety best practice: an application of DEA and Malmquist indices

DEA is used in this paper to investigate target achievements of the operational units of the Norwegian Public Roads Administration (NPRA) charged with traffic safety services. The DEA framework applied corresponds to a BCC model with a unique constant input, or equivalently, with no inputs. This framework is further extended to a DEA-based Malmquist index to measure productivity growth in target achievements. Finally, we use a bootstrapping method to ascertain confidence intervals for efficiency scores derived and to test hypotheses on the extent of productivity growth or regress. The mean efficiency scores by which targets are achieved across the sample years are in the range 0.81–0.93 and significant at the 5% level. Total productivity in target achievements shows progress with significance, on average at 7%. Much of the progress is attributed to technological progress. The results illustrate the usefulness of using a decomposable index for productivity measurement, and the use of bootstrapping for sensitivity tests.     

Maximum Likelihood Estimation of Parameters in a Mixture Model

The estimation of parameters of the log normal distribution based on complete and censored samples are considered in the literature. In this article, the problem of estimating the parameters of log normal mixture model is considered. The Expectation Maximization algorithm is used to obtain maximum likelihood estimators for the parameters, as the likelihood equation does not yield closed form expression. The standard errors of the estimates are obtained. The methodology developed here is then illustrated through simulation studies. The confidence interval based on large-sample theory is obtained.     

Too Connected to Fail? Inferring Network Ties From Price Co-Movements

We use extreme value theory methods to infer conventionally unobservable connections between financial institutions from joint extreme movements in credit default swap spreads and equity returns. Estimated pairwise co-crash probabilities identify significant connections among up to 186 financial institutions prior to the crisis of 2007/2008. Financial institutions that were very central prior to the crisis were more likely to be bailed out during the crisis or receive the status of systemically important institutions. This result remains intact also after controlling for indicators of too-big-to-fail concerns, systemic, systematic, and idiosyncratic risks. Both credit default swap (CDS)-based and equity-based connections are significant predictors of bailouts. Supplementary materials for this article are available online.     

Improved tests for homogeneity of variances

Equality of variances is one of the key assumptions of analysis of variances (ANOVA). There are several testing procedures available to validate this assumption, but it is rare to find a test procedure which controls the type I error rate while providing high statistical power. In this article, we introduce a bootstrap test based on the ratio of mean absolute deviances (RMD). We also propose a two-stage testing procedure where we first quantify the skewness of the distributions and then choose an appropriate test for homogeneity of variances. The performances of these test procedures are studied via a simulation study.     

The Probability Distribution of Future Demand

The bootstrapping technique of Efron (1979) is used to estimate the probability distribution of future electricity demand for Hydro Quebec. The application follows the regression approach of Freedman and Peters (1984a,b) but also allows for serially correlated disturbances and uncertainty in the independent variable forecasts.     

Decriminalization and Marijuana Smoking Prevalence: Evidence From Australia

This article uses the 2001 National Drug Strategy Household Survey to assess the impact of marijuana decriminalization policy on marijuana smoking prevalence in Australia. Both parametric and nonparametric methods are used. The parametric approach includes endogenous probit switching, two-part, sample selection, and standard dummy variable models, while the nonparametric approach uses propensity score stratification matching. Specification analyses are also conducted. A nonparametric kernel-based test is constructed to select between parametric and nonparametric models, and the likelihood ratio test is used to choose among parametric models. Our analyses favor the endogenous switching model where decriminalization increases the probability of smoking by 16.2%.