Shifting paradigms in official statistics: from design-based to model-based to algorithmic inference

Inference in official statistics is traditionally motivated from a design-based perspective, with the model-based approach being gradually adopted in specific circumstances. We take this shifting paradigm one step further, from model-based to algorithmic inference methods. Surveying a sample of the population of interest – typically enterprises or households – is fundamental to the design-based approach, where the design is the basis for inference. Model-based estimation methods may provide a viable alternative in situations where design information is not available. Estimation of the model parameters is pivotal, although in official statistics it is only an intermediate goal, as the model is ultimately used for prediction. Therefore, adopting a data-centred, algorithmic view rather than a model-centred view is possible. The algorithmic view encompasses methods generally attributed to the fields of data mining, machine learning, or statistical learning. Algorithmic methods may be useful in situations where data are not obtained through a sample survey, and where the typical models used in model-based estimation are not tenable.

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