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Testing stability in functional event observations with an application to IPO performance

Horváth, L., Liu, Z., Rice, G., Wang, S. ORCID: https://orcid.org/0000-0003-2113-5521 and Zhan, Y. (2022) Testing stability in functional event observations with an application to IPO performance. Journal of Business and Economic Statistics. ISSN 0735-0015

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To link to this item DOI: 10.1080/07350015.2022.2118127

Abstract/Summary

Many sequentially observed functional data objects are available only at the times of certain events. For example, the trajectory of stock prices of companies after their initial public offering (IPO) can be observed when the offering occurs, and the resulting data may be affected by changing circumstances. It is of interest to investigate whether the mean behaviour of such functions is stable over time, and if not, to estimate the times at which apparent changes occur. Since the frequency of events may fluctuates over time, we propose a change point analysis that has two steps. In the first step, we segment the series into segments in which the frequency of events is approximately homogeneous using a new binary segmentation procedure for event frequencies. After adjusting the observed curves in each segment based on the frequency of events, we proceed in the second step by developing a method to test for and estimate change points in the mean of the observed functional data objects. We establish the consistency and asymptotic distribution of the change point detector and estimator in both steps, and study their performance using Monte Carlo simulations. An application to IPO performance data illustrates the proposed methods.

Item Type:Article
Refereed:Yes
Divisions:Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
ID Code:106918
Publisher:Taylor & Francis

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