Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute?
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915990039691264 |
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| author | Pumi, Guilherme Ulloa, Gladys Choque Prass, Taiane Schaedler |
| author_facet | Pumi, Guilherme Ulloa, Gladys Choque Prass, Taiane Schaedler |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_04754 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute? Pumi, Guilherme Ulloa, Gladys Choque Prass, Taiane Schaedler Methodology Computation 62M10, 62D10, 62F10, 62E20, 60G15 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. |
| title | Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute? |
| topic | Methodology Computation 62M10, 62D10, 62F10, 62E20, 60G15 |
| url | https://arxiv.org/abs/2303.04754 |