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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제30권 제3호
- 발행연도
- 2023.5
- 수록면
- 273 - 289 (17page)
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초록· 키워드
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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 ecient in forecasting monthly IPO volumes.
목차
Abstract
1. Introduction
2. Integer-Valued heterogeneous autoregressive (INHAR) model
3. Estimations
4. Monte-Carlo simulation
5. Empirical example
6. Conclusion
References
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UCI(KEPA) : I410-ECN-0102-2023-310-001919176