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arXiv · 2303.04754

Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute?

Guilherme Pumi·Gladys Choque Ulloa·Taiane Schaedler Prass
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Abstract

Among the most important models for long-range dependent time series is the class of ARFIMA$(p,d,q)$ (Autoregressive Fractionally Integrated Moving Average) models. Estimating the long-range dependence parameter $d$ in ARFIMA models is a well-studied problem, but the literature regarding the estimation of $d$ in the presence of missing data is very sparse. There are two basic approaches to dealing with the problem: missing data can be imputed using some plausible method, and then the estimation can proceed as if no data were missing, or we can use a specially tailored methodology to estimate $d$ in the presence of missing data. In this work, we review some of the methods available for both approaches and compare them through a Monte Carlo simulation study. We present a comparison among 35 different setups to estimate $d$, under tenths of different scenarios, considering percentages of missing data ranging from as few as 10\% up to 70\% and several levels of dependence.

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Guilherme Pumi, Gladys Choque Ulloa, Taiane Schaedler Prass. 2023-03-08. Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute?. https://arxiv.org/abs/2303.04754

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