Using a microbased superpopulation approach some aspects of optimal prediction of aggregated AR(1) processes are studied.

Analysts using data from official statistical authorities often neglect the fact that data frequently are collected using sample surveys. In this paper the impact of sampling error on the estimation of the autocovariance and the autocorrelation function is studied under a micro based...

Analysts using data from official statistical authorities often neglect the fact that data frequently is collected using sample surveys. We study the impact of sampling error on the estimation of the autocorrelation function for a population total under a microbased superpopulation time series...

In Cassel and Lundquist (1990) the existence of sampling bias in estimating autocorrelation functions was discussed under a superpopulation model. One restriction of that model was that the time series model should not exhibit trend. In this paper we relax that restriction. The bias of a...

Using a microbased superpopulation approach (see Cassel and Lundquist (1991), (1990))the question of optimal predictors of a population total of AR(1) series is analysed. Only a sample of the individual timeseries in the population is observed. From the sample the population total is predicted....

An estimator of population parameters for cross sectional analysis is suggested. The estimator is basically the generalised regression estimator (TGR) but with an adjustment derived from optimal predictors of AR(1)-series. The P- properties of the estimator are studied and the efficiency of the...

The question of minimizing the bias due to the survey sampling error when estimating the autocorrelation function of aggregated AR(1) processes is studied.