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http://dx.doi.org/10.29220/CSAM.2020.27.2.177

Forecasting evaluation via parametric bootstrap for threshold-INARCH models  

Kim, Deok Ryun (Department of Statistics, Sookmyung Women's University)
Hwang, Sun Young (Department of Statistics, Sookmyung Women's University)
Publication Information
Communications for Statistical Applications and Methods / v.27, no.2, 2020 , pp. 177-187 More about this Journal
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.
Keywords
integer-valued time series; one-step ahead forecasting; parametric bootstrap;
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1 Ali M, Debes AK, Luquero FJ, Kim DR, Park JY, and Digilio L (2016). Potential for controlling cholera using a ring vaccination strategy: re-analysis of data from a cluster-randomized clinical trial, PLOS Medicine, 13, e1002120.   DOI
2 Ali M, Kim DR, Yunus M, and Emch M(2013). Time series analysis of cholera in Matlab, Bangladesh, during 1988-2001, Journal of Health, Population, and Nutrition, 31, 11-19.
3 Andre FE, Booy R, Bock HL, Clemens J, Datta SK, John TJ, and Schmitt HJ (2008). Vaccination greatly reduces disease, disability, death and inequity worldwide, Bulletin of the World Health Organization, 86, 140-146.   DOI
4 Azman AS, Rudolph KE, Cummings DAT, and Lessler J (2013). The incubation period of cholera: a systematic review, Journal of Infection, 66, 432-438.   DOI
5 Bourguignon M, Rodrigues J, and Santos-Neto M (2019). Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion, Journal of Applied Statistics, 46, 101-118.   DOI
6 Cardinal M, Roy R, and Lambert J (1999). On the application of integer-valued time series models for the analysis of disease incidence, Statistics in Medicine, 18, 2025-2039.   DOI
7 Ferland R, Latour A, and Oraichi D (2006). Integer-valued GARCH process, Journal of Time Series Analysis, 27, 923-942.   DOI
8 Hyndman RJ and Koehler AB (2006). Another look at measures of forecast accuracy, International Journal of Forecasting, 22, 679-688.   DOI
9 Kim DR, Yoon JE, and Hwang SY (2019). Threshold-asymmetric volatility models for integer-valued time series, Communications for Statistical Applications and Methods, 26, 295-304.   DOI
10 AB and Terasvirta T (2010). Forecasting with nonlinear time series models (CREATES Research Papers), Department of Economics and Business Economics, Aarhus University, 2010-01.
11 Liu M, Li Q, and Zhu F (2019). Threshold negative binomial autoregressive model, Statistics, 53, 1-25.   DOI
12 Saha A and Dong D (1997). Estimating nested count data models, Oxford Bulletin of Economics and Statistics, 59, 423-430.   DOI
13 Taieb SB, Bontempi G, Atiya AF, and Sorjamaa A (2012). A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition, Expert Systems with Applications, 39, 7067-7083.   DOI
14 Timm NH (2002). Applied Multivariate Analysis (2nd ed), Springer, New York.
15 Tsay RS (2010). Analysis of Financial Time Series (3rd Ed), John Wiley & Sons, Hoboken.
16 Weib CH (2010). The INARCH(1) model for overdispersed time series of counts, Communications in Statistics: Simulation and Computation, 39, 1269-1291.   DOI
17 Yoon JE and Hwang SY (2015). Integer-valued GARCH models for count time series: case study, Korean Journal of Applied Statistics, 28, 115-122.   DOI
18 Zhu F (2012). Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued GARCH models, Journal of Mathematical Analysis and Applications, 389, 58-71.   DOI
19 Zhang X, Liu Y, Yang M, et al. (2013). Comparative study of four time series methods in forecasting typhoid fever incidence in China, PLoS ONE, 8, e63116.   DOI
20 Zhu F (2011). A negative binomial integer-valued GARCH model, Journal of Time Series Analysis, 32, 54-67.   DOI
21 Wang C, Liu H, Yao JF, Davis RA, and Li WK (2014). Self-excited threshold Poisson autoregression, Journal of the American Statistical Association, 109, 777-787.   DOI