1Department of Communicable Disease Control and Prevention, Jämtland County Council, Östersund, Sweden; 2Department of Clinical Microbiology, Unit of Clinical Research CenterÖstersund, Umeå University, Umeå, Sweden; 3Centre of Registers in Northern Sweden, Umeå University, Umeå, Sweden; 4Swedish Health Care Direct 1177, Jämtland County Council, Östersund, Sweden; 5Department of Clinical Microbi...

Since the publication of the Bellcore measurements in the early nineties, long-range dependence (LRD) has been in the center of a continuous debate within the teletraac community. While researchers largely acknowledge the signiicance of the LRD phenomenon, they still disagree on two issues: (1) the utility of LRD models in buuer dimensioning and bandwidth allocation, and (2) the ability of comm...

Morehead City, North Carolina, faced a moratorium on new construction because of problems with its sewage system. We developed an autoregressive integrated moving average (ARIMA) transfer function-intervention model to help town officials explain the sources of variation in the volume of sewage-treatment-plant discharge to the state officials who had imposed the moratorium on extending the sewa...

In many intervention analysis applications, time series data may be expensive or otherwise difficult to collect. In this case the power function is helpful, because it can be used to determine the probability that a proposed intervention analysis application will detect a meaningful change. Assuming that an underlying autoregressive integrated moving average (ARIMA) or fractional ARIMA model is...

Durbin’s methods for moving average (MA) and autoregressive-moving average (ARMA) estimation use the parameters of a long AR model to compute the MA parameters. Linear regression theory is applied to find the best AR order. This yields two different orders: one for the best predicting AR model and another one for the long AR model with the best parameter accuracy, as intermediate for Durbin’s e...

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better sui...

This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the er...

The purpose of the paper is to extend recent results of Muellbauer (1986) for the life cycle consumption model under rational habit formation. It is shown that a general pattern of habits will lead to an arbitrary autoregressive integrated moving average (ARIMA) process for consumption. As a special case it is shown how a model in annual differences can be obtained by imposing a specific struct...

The paper presents an analysis of the seasonality of Italian daily electricity prices. Since the correct detection of the nature, stochastic or probabilistic, of the seasonality is crucial in ARIMA modeling, a test that allows such detection is presented. The application of this test to the Italian daily prices in the years 2008-11 has pointed to the presence of deterministic seasonality in the...

This article analyses the school performance of migrants dependent on peer groups in different international schooling environments. Using data from the international OECD PISA test, we consider social interaction within and between groups of natives and migrants. Results based on social multipliers (Glaeser et al. 2000, 2003) suggest that both native-tonative and migrant-to-migrant peer effect...