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Ratio-based estimators for a change point in persistence.

Andreea G Halunga1, Denise R Osborn

  • 1Department of Economics, University of Exeter Business School, Rennes Drive, Exeter EX4 4PU, United Kingdom.

Journal of Econometrics
|June 28, 2013
PubMed
Summary
This summary is machine-generated.

Ratio-based estimators for detecting changes in time series persistence are inconsistent when a mean is estimated. These estimators exhibit downward bias, particularly in smaller samples, impacting accurate change point detection.

Keywords:
Order of integrationPersistence changeStructural breaks

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Area of Science:

  • Econometrics
  • Time Series Analysis
  • Statistical Inference

Background:

  • Accurate detection of changes in time series persistence is crucial for modeling and forecasting.
  • Existing ratio-based breakpoint estimators (Kim, 2000; Kim et al., 2002; Busetti & Taylor, 2004) have been used for this purpose.
  • The impact of estimating deterministic components, such as the mean, on these estimators was not fully understood.

Purpose of the Study:

  • To analytically investigate the consistency of ratio-based breakpoint estimators when a mean is estimated.
  • To evaluate the finite sample performance of these estimators through simulation.
  • To identify potential biases and their sources in change point estimation.

Main Methods:

  • Analytical derivation of estimator properties under mean estimation.
  • Monte Carlo simulations to assess bias and consistency.
  • Comparison of estimator behavior with theoretical predictions.

Main Results:

  • Ratio-based breakpoint estimators are shown to be inconsistent when a mean is estimated.
  • Estimators converge to random variables bounded by the true break date.
  • Monte Carlo studies confirm significant downward bias in both large and moderate sample sizes.

Conclusions:

  • The widely used ratio-based estimators are unreliable when deterministic components are present.
  • The inconsistency and bias necessitate caution when applying these methods for change point detection.
  • Further research may be needed to develop robust estimators for time series with changing persistence and estimated deterministic components.