Key result
Optimal sampling rates for continuous-time autoregressive and vector autoregressive models can be determined to minimize standard errors of parameter estimators based on optimal design theory.
Why the study?
Whether and how the sampling rate affects the quality with which continuous-time autoregressive and vector autoregressive models can be estimated has largely been ignored.
Key points are not available for this paper at this time.
Population
Data from the COGITO Study
Design
Methodological study using optimal design and maximum likelihood estimation theories
Continuous-time VAR models may better handle irregular sampling in affect dynamics; leaves open whether they alter substantive conclusions.
Autoregressive and vector autoregressive models are a driving force in current psychological research. In affect research they are, for instance, frequently used to formalize affective processes and estimate affective dynamics. Discrete-time model variants are most commonly used, but continuous-time formulations are gaining popularity, because they can handle data from longitudinal studies in which the sampling rate varies within the study period, and yield results that can be compared across data sets from studies with different sampling rates. However, whether and how the sampling rate affects the quality with which such continuous-time models can be estimated, has largely been ignored in the literature. In the present article, we show how the sampling rate affects the estimation reliability (i.e., the standard errors of the parameter estimators, with smaller values indicating higher reliability) of continuous-time autoregressive and vector autoregressive models. Moreover, we determine which sampling rates are optimal in the sense that they lead to standard errors of minimal size (subject to the assumption that the models are correct). Our results are based on the theories of optimal design and maximum likelihood estimation. We illustrate them making use of data from the COGITO Study. We formulate recommendations for study planning, and elaborate on strengths and limitations of our approach. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Adolf et al. (2021) studied this question. Optimal sampling rates for continuous-time autoregressive and vector autoregressive models can be determined to minimize standard errors of parameter estimators based on optimal design theory.
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