Assessing the relative power of structural break tests using a framework based on the approximate Bahadur slope
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Publication:302096
DOI10.1016/j.jeconom.2008.10.010zbMath1429.62076OpenAlexW2051183070MaRDI QIDQ302096
Publication date: 4 July 2016
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jeconom.2008.10.010
Asymptotic distribution theory in statistics (62E20) Asymptotic properties of parametric tests (62F05)
Related Items (18)
Asymptotic Pitman's Relative Efficiency ⋮ GENERALIZED LAPLACE INFERENCE IN MULTIPLE CHANGE-POINTS MODELS ⋮ Structural change tests under heteroskedasticity: Joint estimation versus two‐steps methods ⋮ Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models ⋮ Simultaneous bandwidths determination for DK-HAC estimators and long-run variance estimation in nonparametric settings ⋮ Testing for multiple structural changes with non-homogeneous regressors ⋮ Changepoint Detection in Heteroscedastic Random Coefficient Autoregressive Models ⋮ Testing for factor loading structural change under common breaks ⋮ Testing for common breaks in a multiple equations system ⋮ Continuous record Laplace-based inference about the break date in structural change models ⋮ Improved Tests for Forecast Comparisons in the Presence of Instabilities ⋮ Testing for parameter constancy in the time series direction in panel data models ⋮ Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements ⋮ Bahadur intercept with applications to one-sided testing ⋮ Quantile regression estimates and the analysis of structural breaks ⋮ A comparison of alternative methods to construct confidence intervals for the estimate of a break date in linear regression models ⋮ On the Usefulness or Lack Thereof of Optimality Criteria for Structural Change Tests ⋮ Testing for parameter instability and structural change in persistent predictive regressions
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