Forecasting evaluation via parametric bootstrap for threshold-INARCH modelsopen access
- Authors
- Kim, Deok Ryun; Hwang, Sun Young
- Issue Date
- Mar-2020
- Publisher
- 한국통계학회
- Citation
- Communications for Statistical Applications and Methods, v.27, no.2, pp 177 - 187
- Pages
- 11
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 27
- Number
- 2
- Start Page
- 177
- End Page
- 187
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1568
- DOI
- 10.29220/CSAM.2020.27.2.177
- ISSN
- 2287-7843
- Abstract
- This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.
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