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Prediction Regions for Interval-valued Time Series

Gloria Gonzalez-Rivera (), Yun Luo () and Esther Ruiz
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Yun Luo: University of California, Riverside

No 201817, Working Papers from University of California at Riverside, Department of Economics

Abstract: We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches to construct bivariate prediction regions of the interval center and range (or lower/upper bounds). We estimate a bivariate system of the center/log-range, which may not be normally distributed. Implementing analytical or bootstrap methods, we directly transform prediction regions for center/log-range into those for center/range and upper/lower bounds systems. We propose new metrics to evaluate the regions performance. Monte Carlo simulations show bootstrap methods being preferred even in Gaussian systems. For daily SP500 low/high return intervals, we build joint conditional prediction regions of the return level and return volatility.

Keywords: Bootstrap; Constrained Regression; Coverage Rates; Logarithmic Transformation; QML estimation (search for similar items in EconPapers)
JEL-codes: C01 C22 C53 (search for similar items in EconPapers)
Pages: 52 Pages
Date: 2018-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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https://economics.ucr.edu/repec/ucr/wpaper/201817.pdf First version, 2018 (application/pdf)

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Working Paper: Prediction Regions for Interval-valued Time Series (2019) Downloads
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