Change-point analysis for binomial autoregressive model with application to price stability counts

TL;DR

This paper develops a change-point detection method for non-stationary binomial autoregressive count data, enhancing analysis of price stability indices by identifying shifts in process parameters.

Contribution

It introduces a BAR(1) model with multiple change-points and employs CUSUM and MDL for detection and estimation, extending the traditional stationary BAR(1) model.

Findings

Effective detection of change-points in price data

Application to EU consumer price index analysis

Demonstrated improved modeling of non-stationary count data

Abstract

The first-order binomial autoregressive (BAR(1)) model is the most frequently used tool to analyze the bounded count time series. The BAR(1) model is stationary and assumes process parameters to remain constant throughout the time period, which may be incompatible with the non-stationary real data, which indicates piecewise stationary characteristic. To better analyze the non-stationary bounded count time series, this article introduces the BAR(1) process with multiple change-points, which contains the BAR(1) model as a special case. Our primary goals are not only to detect the change-points, but also to give a solution to estimate the number and locations of the change-points. For this, the cumulative sum (CUSUM) test and minimum description length (MDL) principle are employed to deal with the testing and estimation problems. The proposed approaches are also applied to analysis of the Harmonised Index of Consumer Prices of the European Union.