Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models
We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time series regression models. Although, the particular comparisons of these two different modelling environments is done from the perspective of the partial-sum processes, it emphasizes that the presence of nuisance parameters can change the asymptotic behaviour of the functionals under consideration. Simulation experiments verify size distortions when testing for a break in nonstationary time series regressions which indicates that the Brownian bridge limit cannot provide a suitable asymptotic approximation in this case. Further research is required to establish the cause of size distortions under the null hypothesis of parameter stability.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionTime SeriesTime Series AnalysisTime Series RegressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Testing for a Forecast Accuracy Breakdown under Long Memory
We propose a test to detect a forecast accuracy breakdown in a long memory time series and provide theoretical and simulation evidence on the memory transfer from the time series to the forecast residuals. The proposed m…
Time SeriesLikelihood ratio test for structural changes in factor models
A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but a change in the variance of its factors. This effectively transforms a structural change pro…
Model-based causal feature selection for general response types
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which requires data from het…
feature selectionAn SO(3)-equivariant reciprocal-space neural potential for long-range interactions
Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Alth…
Predictability Tests Robust against Parameter Instability
We consider Wald type statistics designed for joint predictability and structural break testing based on the instrumentation method of Phillips and Magdalinos (2009). We show that under the assumption of nonstationary pr…