Detecting At-Most-m-Changes in Linear Regression Models

Lajos Horvath, William Pouliot, Shixuan Wang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
134 Downloads (Pure)

Abstract

In this paper we provide a new procedure to test for at most m changes in the
time–dependent regression model Our procedure is based on weighted sums of the residuals, incorporating the possibility of m changes. The weak limit of the proposed test statistic is the sum of two double exponential random variables. A small Monte Carlo simulation illustrates the applicability of the limit results in case of small and moderate sample sizes. We compare the new method to the CUSUM and standardized (weighted) CUSUM procedures and obtain
the power curves of the test statistics under the alternative. We apply our method to find changes in the unconditional four factor CAPM.
Original languageEnglish
Pages (from-to)552-590
Number of pages39
JournalJournal of Time Series Analysis
Volume38
Issue number4
Early online date26 Dec 2016
DOIs
Publication statusPublished - Jul 2017

Keywords

  • Change point
  • Bernoulli shifts
  • weak approximation
  • weighted CUSUM
  • residuals
  • linear regression models

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