MONITORING PROCEDURES TO DETECT UNIT ROOTS AND STATIONARITY

Ansgar Steland

22 citations0/0 files storedEconometric Theory

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Monetary Policy and Economic Impact Advanced Statistical Process Monitoring Diverse Scientific and Economic Studies

Abstract

When analyzing time series an important issue is to decide whether the time series is stationary or a random walk. Relaxing these notions, we consider the problem to decide in favor of the I(0) or I(1) property. Fixed-sample statistical tests for that problem are well studied in the literature. In this paper we provide first results for the problem of monitoring sequentially a time series. Our stopping times are based on a sequential version of a kernel-weighted variance-ratio statistic. The asymptotic distributions are established for I(1) processes, a rich class of stationary processes, possibly affected by local nonparametric alternatives, and the local-to-unity model. Further, we consider the two interesting change-point models where the time series changes its behavior after a certain fraction of the observations and derive the associated limiting laws. Our Monte Carlo studies show that the proposed detection procedures have high power when interpreted as a hypothesis test and that the decision can often be made very early.The financial support of the DFG (Deutsche Forschungsgemeinschaft, SFB 475, Reduction of Complexity in Multivariate Data Structures) is gratefully acknowledged. I thank two anonymous referees for constructive and helpful remarks that improved the paper and Dipl.-Math. Sabine Teller for proofreading a revised version.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 22 citations exist upstream.

References

1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root 1988Testing for a Unit Root in Time Series Regression 1992Testing the null hypothesis of stationarity against the alternative of a unit root 1979Distribution of the Estimators for Autoregressive Time Series With a Unit Root 1991Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation 1987Time Series Regression with a Unit Root 1989Testing for the Constancy of Parameters Over Time 1999Some tests for parameter constancy in cointegrated VAR‐models

Cited by

2010Monitoring change in persistence in linear time series 2021SEQUENTIAL MONITORING OF CHANGES IN DYNAMIC LINEAR MODELS, APPLIED TO THE U.S. HOUSING MARKET 2013Nonparametric Sequential Signal Change Detection Under Dependent Noise 2007Weighted Dickey–Fuller processes for detecting stationarity 2017Consistent Monitoring of Cointegrating Relationships: The US Housing Market and the Subprime Crisis

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.