An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have neve...An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have never been exploited for establishment surveys. However, the rapidly increasing availability of geo- referenced information about business units makes that possible. In business studies, it may indeed be important to take into account the presence of spatial autocorrelation or spatial trends in the variables of interest, in order to have more precise and efficient estimates. The opportunity of using the most innovative spatial sampling designs in business surveys, in order to produce samples that are well spread in space, is here tested by means of Monte Carlo experiments. For all designs, the Horvitz-Thompson estimator of the population total is used both with equal and unequal inclusion probabilities. The efficiency of sampling designs is evaluated in terms of relative RMSE and efficiency gain compared with designs ignoring the spatial information. Furthermore, an evaluation of spatially balancing samples is also conducted.展开更多
文摘An innovative use of spatial sampling designs is here presented. Sampling methods which consider spatial locations of statistical units are already used in agricultural and environmental contexts, while they have never been exploited for establishment surveys. However, the rapidly increasing availability of geo- referenced information about business units makes that possible. In business studies, it may indeed be important to take into account the presence of spatial autocorrelation or spatial trends in the variables of interest, in order to have more precise and efficient estimates. The opportunity of using the most innovative spatial sampling designs in business surveys, in order to produce samples that are well spread in space, is here tested by means of Monte Carlo experiments. For all designs, the Horvitz-Thompson estimator of the population total is used both with equal and unequal inclusion probabilities. The efficiency of sampling designs is evaluated in terms of relative RMSE and efficiency gain compared with designs ignoring the spatial information. Furthermore, an evaluation of spatially balancing samples is also conducted.
