Temporal aggregation in a periodically integrated autoregressive process

Time Series;Aggregation;statistics

On the optimality of expert-adjusted forecasts

Official forecasts of international institutions are never purely model-based. Preliminary results of models are adjusted with expert opinions. What is the impact of these adjustments for the forecasts? Are they necessary to get 'optimal' forecasts? When model-based forecasts are adjusted by experts, the loss function of these forecasts is not a mean squared error loss function. In fact, the overall loss function is unknown. To examine the quality of these forecasts, one can rely on the tests for forecast optimality under unknown loss function as developed in Patton and Timmermann (2007). We apply one of these tests to ten variables for which we have model-based forecasts and expert-adjusted forecasts, all generated by the Netherlands Bureau for Economic Policy Analysis (CPB). For almost all variables the added expertise yields better forecasts in terms of fit. In terms of optimality, the effect of adjustments for the forecasts is limited, because for most variables the assumption that the forecast are not optimal can be rejected for both the model-based and the expert-adjusted forecasts.

Modelling health care expenditures; overview of the literature and evidence from a panel time series model

The first purpose of this paper is to give an up to date overview of the literature on health care expenditures. Secondly, this paper contributes to the existing literature by investigating the impact of several factors on health care expenditures in an empirical analysis using an error-correction model. Health care expenditures in industrial countries have been growing rapidly over the past forty years. This rapid growth jeopardizes the sustainability of public budgets and causes an increasing interest in the determinants of health care expenditures. Additional to the 'usual suspects' for rising health care expenditures, we pay attention to a somewhat neglected driving factor, which is the increase in the relative price of health care compared to other goods and services. We find that the increasing price of health care helps to explain the increase in real health care expenditures. However, the use of health care in volume terms is negatively affected by the increasing price. This effect seems to be stronger in periods of cost containment policy. Consistent with most recent findings in the literature, we find that income and ageing are important drivers of health care expenditures.