Modeling and Forecasting of Economic Dynamics by Complex-Valued Models

Svetunkov, Sergey

doi:10.1007/978-1-4614-5876-0_9

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2023

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“…In particular, “p“ and “l” are parametric methods based on the normal error assumption with an unbiased in‐sample error variance estimate and a biased variance estimate from the likelihood, respectively. The semi‐parametric method “sp” is based on the covariance matrix of one‐ to

k

‐step‐ahead errors and the assumption of normal or log‐normal distribution; see Svetunkov (2021). The three methods from es are denoted as “esp,” “esl,” and “essp,” respectively.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Empirical prediction intervals for additive Holt–Winters methods under misspecification

Yang,

Tang,

Yau

2023

Journal of Forecasting

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Holt–Winters (HW) methods have been widely used by practitioners for the prediction of time series. However, traditional prediction intervals associated with the HW methods are only theoretically justified for a few types of SARIMA processes. In this article, we propose an empirical prediction interval for a general class of prediction procedures containing the HW methods as special cases. We establish the asymptotic validity of the prediction intervals under mild conditions, which allow model misspecification. Simulation experiments and an application to financial time series are provided to illustrate the good performance of the prediction intervals.

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