Prediction Regions for Interval-valued Time Series
Gloria Gonzalez-Rivera (),
Yun Luo () and
Additional contact information
Yun Luo: University of California, Riverside
No 201817, Working Papers from University of California at Riverside, Department of Economics
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
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://economics.ucr.edu/repec/ucr/wpaper/201817.pdf First version, 2018 (application/pdf)
Working Paper: Prediction Regions for Interval-valued Time Series (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201817
Access Statistics for this paper
More papers in Working Papers from University of California at Riverside, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Kelvin Mac ().