Aegon (United States)

To quantify uncertainty around point estimates of conditional objects such as conditional means or variances, parameter uncertainty has to be taken into account. Attempts to incorporate parameter uncertainty are typically based on the unrealistic assumption of observing two independent processes, where one is used for parameter estimation, and the other for conditioning upon. Such unrealistic foundation raises the question whether these intervals are theoretically justified in a realistic setting. This paper presents an asymptotic justification for this type of intervals that does not require such an unrealistic assumption, but relies on a sample-split approach instead. By showing that our sample-split intervals coincide asymptotically with the standard intervals, we provide a novel, and realistic, justification for confidence intervals of conditional objects. The analysis is carried out for a rich class of time series models. We also present the results of a simulation study to evaluate the performance of the sample-split approach. The results indicate that also in practice sample-split intervals might be more appropriate than the standard intervals.

In this study, we examine the sustainability of downtown parking in the likely advent of driverless vehicles (DVs). The confluence of driverless, electric, and fleet-owned cars is likely to influence personal car use choice of travel modes. We ask where driverless cars could be staged during the day. We review 21 available practitioner forecasts on DV adoption, and project that net demand for parking in downtown Cleveland would drop by 20%–66% by 2035. The servicing and staging needs for the expected “robotaxi” fleet of 1,300–4,300 vehicles could be accommodated by city-owned overflow parking by the lakefront airport or football sports stadium.