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Friday, May 31
Computational Statistics
Change Point Detection
Fri, May 31, 5:20 PM - 6:25 PM
Grand Ballroom K

Detection of Structural Changes in Correctly Specified and Misspecified Conditional Quantile Polynomial Distributed Lag (QPDL) Model Using Change-Point Analysis (305132)



Keywords: Binseg, Cusum, Structural Changes, Misspecification, Mean-shift

Change-point analysis is a powerful tool for determining whether a change has taken place or not. In this paper we study the structural changes in the Conditional Quantile Polynomial Distributed Lag (QPDL) model using change-point analysis. We employ both the Binary Segmentation (BinSeg) and Cumulative Sum (Cusum) methods for this analysis. We studied the structural changes in both correctly specified and misspecified QPDL models. As an economic application we considered the production of rubber and its price returns. We observe that both Cusum and BinSeg methods correctly detected the structural changes for both the correctly specified and the misspecified QPDL model. The Cusum method gave the exact positions where the structural changes occurred and the BinSeg gave the approximated positions where the changes occurred. Both methods were able to detect the shift in time for both the mean and variance for the missspecified QPDL model, hence both methods were better for predicting structural stability in a QPDL models. The impact of this is that, when there are changes made to a data knowingly or unknowingly, they can be detected, as well as when these changes were effected. We further observed that both methods were powerful tools that better characterizes the changes, controls the overall error rate, robust to outliers, more flexible and simple to use.