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Open access

Bart Buelens and Jan A. Van den Brakel

Abstract

Nowadays sample survey data collection strategies combine web, telephone, face-to-face, or other modes of interviewing in a sequential fashion. Measurement bias of survey estimates of means and totals are composed of different mode-dependent measurement errors as each data collection mode has its own associated measurement error. This article contains an appraisal of two recently proposed methods of inference in this setting. The first is a calibration adjustment to the survey weights so as to balance the survey response to a prespecified distribution of the respondents over the modes. The second is a prediction method that seeks to correct measurements towards a benchmark mode. The two methods are motivated differently but at the same time coincide in some circumstances and agree in terms of required assumptions. The methods are applied to the Labour Force Survey in the Netherlands and are found to provide almost identical estimates of the number of unemployed. Each method has its own specific merits. Both can be applied easily in practice as they do not require additional data collection beyond the regular sequential mixed-mode survey, an attractive element for national statistical institutes and other survey organisations.

Open access

Reinier Bikker, Jan van den Brakel, Sabine Krieg, Pim Ouwehand and Ronald van der Stegen

Abstract

Seasonally adjusted series of Gross Domestic Product (GDP) and its breakdown in underlying categories or domains are generally not consistent with each other. Statistical differences between the total GDP and the sum of the underlying domains arise for two reasons. If series are expressed in constant prices, differences arise due to the process of chain linking. These differences increase if, in addition, a univariate seasonal adjustment, with for instance X-13ARIMA-SEATS, is applied to each series separately. In this article, we propose to model the series for total GDP and its breakdown in underlying domains in a multivariate structural time series model, with the restriction that the sum over the different time series components for the domains are equal to the corresponding values for the total GDP. In the proposed procedure, this approach is applied as a pretreatment to remove outliers, level shifts, seasonal breaks and calendar effects, while obeying the aforementioned consistency restrictions. Subsequently, X-13ARIMA-SEATS is used for seasonal adjustment. This reduces inconsistencies remarkably. Remaining inconsistencies due to seasonal adjustment are removed with a benchmarking procedure.