Sayar Karmakar, Marek Chudy, Wei Biao Wu
arXiv 15 Dec 2020 · Statistics — Methodology · publishedJournal of Time Series Analysis (2021) · 1 citations (OpenAlex)
arXiv:2012.08223 · PDF · DOI · OpenAlex · Extracted main text
Accurate forecasting is one of the fundamental focus in the literature of econometric time-series. Often practitioners and policy makers want to predict outcomes of an entire time horizon in the future instead of just a single $k$-step ahead prediction. These series, apart from their own possible non-linear dependence, are often also influenced by many external predictors. In this paper, we construct prediction intervals of time-aggregated forecasts in a high-dimensional regression setting. Our approach is based on quantiles of residuals obtained by the popular LASSO routine. We allow for general heavy-tailed, long-memory, and nonlinear stationary error process and stochastic predictors. Through a series of systematically arranged consistency results we provide theoretical guarantees of our proposed quantile-based method in all of these scenarios. After validating our approach using simulations we also propose a novel bootstrap based method that can boost the coverage of the theoretical intervals. Finally analyzing the EPEX Spot data, we construct prediction intervals for hourly electricity prices over horizons spanning 17 weeks and contrast them to selected Bayesian and bootstrap interval forecasts.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Müller, U. and M. Watson (2016) Measuring uncertainty about long-run predictions | 1.000 | 8 | 3 | 100% |
| 2 | Chudý, M., S. Karmakar, and W. B. Wu (2020) Long-term prediction intervals of economic time series | 1.000 | 5 | 3 | 100% |
| 3 | Zhou, Z., Z. Xu, and W. B. Wu (2010) Long-term prediction intervals of time series | 0.974 | 13 | 6 | 92% |
| 4 | Wu, W. B (2005) Nonlinear system theory: another look at dependence self | 0.928 | 5 | 3 | 80% |
| 5 | Ludwig, N., S. Feuerriegel, and D. Neumann (2015) Putting big data analytics to work: Feature selection for forecasting electricity prices using the lasso and random forests | 0.644 | 2 | 2 | 100% |
| 6 | Wu, W. B. and Y. N. Wu (2016) Performance bounds for parameter estimates of high-dimensional linear models with correlated errors self | 0.511 | 3 | 2 | 33% |
| 7 | Cover, T. M (1975) Open problems in information theory | 0.405 | 1 | 1 | 100% |
| 8 | Györfi, L., W. Härdle, P. Sarda, and P. Vieu (2013) Nonparametric curve estimation from time series, Volume 60 | 0.405 | 1 | 1 | 100% |
| 9 | Gyorfi, L., G. Lugosi, and G. Morvai (1998) A simple randomized algorithm for consistent sequential prediction of ergodic time series | 0.405 | 1 | 1 | 100% |
| 10 | Gyorfi, L. and G. Ottucsak (2007) Sequential prediction of unbounded stationary time series | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 53 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables | 0.405 | 1 | 1 |
| 2 | High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods | 0.405 | 1 | 1 |