Haowen Bao, Yongmiao Hong, Yuying Sun, Shouyang Wang
arXiv 14 Nov 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2411.09452 · PDF · DOI · OpenAlex · Extracted main text
By treating intervals as inseparable sets, this paper proposes sparse machine learning regressions for high-dimensional interval-valued time series. With LASSO or adaptive LASSO techniques, we develop a penalized minimum distance estimation, which covers point-based estimators are special cases. We establish the consistency and oracle properties of the proposed penalized estimator, regardless of whether the number of predictors is diverging with the sample size. Monte Carlo simulations demonstrate the favorable finite sample properties of the proposed estimation. Empirical applications to interval-valued crude oil price forecasting and sparse index-tracking portfolio construction illustrate the robustness and effectiveness of our method against competing approaches, including random forest and multilayer perceptron for interval-valued data. Our findings highlight the potential of machine learning techniques in interval-valued time series analysis, offering new insights for financial forecasting and portfolio management.
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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 | Sun, Y., Han, A., Hong, Y., and Wang, S (2018) Threshold autoregressive models for interval-valued time series data self | 1.000 | 7 | 4 | 100% |
| 2 | He, Y., Han, A., Hong, Y., Sun, Y., and Wang, S (2021) Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models self | 1.000 | 6 | 4 | 100% |
| 3 | Han, A., Hong, Y., Wang, S., and Yun, X (2016) A vector autoregressive moving average model for interval-valued time series data self | 0.950 | 7 | 5 | 86% |
| 4 | González-Rivera, G. and Lin, W (2013) Constrained regression for interval-valued data | 0.874 | 6 | 2 | 100% |
| 5 | Neto, E. d. A. L. and de Carvalho, F. d. A (2008) Centre and range method for fitting a linear regression model to symbolic interval data | 0.843 | 3 | 3 | 100% |
| 6 | Fan, J. and Peng, H (2004) Nonconcave penalized likelihood with a diverging number of parameters | 0.737 | 4 | 2 | 75% |
| 7 | Zou, H (2006) The adaptive lasso and its oracle properties | 0.737 | 3 | 3 | 67% |
| 8 | Yang, Z., Lin, D. K., and Zhang, A (2019) Interval-valued data prediction via regularized artificial neural network | 0.737 | 3 | 2 | 100% |
| 9 | Yang, W., Han, A., Hong, Y., and Wang, S (2016) Analysis of crisis impact on crude oil prices: a new approach with interval time series modelling self | 0.737 | 3 | 2 | 100% |
| 10 | Han, A., Hong, Y., Sun, Y., and Wang, S (2020) Autoregressive conditional models for interval-valued time series data self | 0.737 | 3 | 2 | 100% |
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