MONITORING PROCEDURES TO DETECT UNIT ROOTS AND STATIONARITY

DOI10.1017/S0266466607070442zbMath1237.62126arXiv1001.1831OpenAlexW2084654022MaRDI QIDQ2886978

Ansgar Steland

Publication date: 14 May 2012

Published in: Econometric Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1001.1831

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Stationary stochastic processes (60G10) Monte Carlo methods (65C05) Optimal stopping in statistics (62L15)

Related Items

SEQUENTIAL MONITORING OF CHANGES IN DYNAMIC LINEAR MODELS, APPLIED TO THE U.S. HOUSING MARKET, Monitoring persistent change in a heavy-tailed sequence with polynomial trends, Sequentially Updated Residuals and Detection of Stationary Errors in Polynomial Regression Models, Weighted Dickey-Fuller processes for detecting stationarity, Monitoring change in persistence in linear time series, Discussion on “Quickest Detection Problems: Fifty Years Later” by Albert N. Shiryaev, Monitoring persistence change in infinite variance observations, Structural breaks in time series, Detection of Stationary Errors in Multiple Regressions with Integrated Regressors and Cointegration

Cites Work

• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
• Gradual changes versus abrupt changes.
• Nonparametric detection of changepoints for sequentially observed data
• Deciding between I(1) and I(0)
• Testing the unit root with drift hypothesis against nonlinear trend stationarity, with an application to the US price level and interest rate
• Sequential control of time series by functionals of kernel-weighted empirical processes under local alternatives
• On the distribution of the clipping median under a mixture model
• Optimal sequential kernel detection for dependent processes
• Nonparametric tests for unit roots and cointegration.
• Rescaled variance and related tests for long memory in volatility and levels
• Monitoring changes in linear models
• On the Absolute Continuity of Distributions of Functionals of Random Processes
• Testing for a unit root in time series regression
• A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
• Testing for the Constancy of Parameters Over Time
• The Cusum Test with Ols Residuals
• Testing For and Dating Common Breaks in Multivariate Time Series
• Reducing size distortions of parametric stationarity tests
• Jump-preserving monitoring of dependent time series using pilot estimators
• Some tests for parameter constancy in cointegrated VAR‐models
• Time Series Regression with a Unit Root
• The Cusum Test for Parameter Change in Time Series Models
• On detecting jumps in time series: nonparametric setting
• The joint distribution of the running maximum and its location of D-valued Markov processes
• Random Walks with Drift – A Sequential Approach