Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Integer-Valued HAR(p) model with Poisson distribution for forecasting IPO volumes

  • SeongMin Yu (Department of Applied Statistics, Gachon University) ;
  • Eunju Hwang (Department of Applied Statistics, Gachon University)
  • Received : 2022.08.25
  • Accepted : 2023.03.29
  • Published : 2023.05.31
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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.

Keywords

Acknowledgement

This research was supported by Research Fund of Gachon University (GCU-202104590001).

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