Integer-Valued HAR(p) model with Poisson distribution for forecasting IPO volumesopen access
- Authors
- Yu, SeongMin; Hwang, Eunju
- Issue Date
- May-2023
- Publisher
- Korean Statistical Society
- Keywords
- conditional least squares estimate; initial public offering; integer-valued heterogeneous autoregressive model; Yule-Walker estimate
- Citation
- Communications for Statistical Applications and Methods, v.30, no.3, pp.273 - 289
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 30
- Number
- 3
- Start Page
- 273
- End Page
- 289
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88151
- DOI
- 10.29220/CSAM.2023.30.3.273
- ISSN
- 2287-7843
- Abstract
- In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea’s IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea’s IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes. © 2023 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 사회과학대학 > 응용통계학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.