Abstract
We consider changes in the degree of persistence of a process when the degree
of persistence is characterized as the order of integration of a strongly dependent
process. To avoid the risk of incorrectly specifying the data generating process
we employ local Whittle estimates which uses only frequencies local to zero. The
limit distribution of the test statistic under the null is not standard but it is well
known in the literature. A Monte Carlo study shows that this inference procedure
performs well in finite samples. We demonstrate the practical utility of these
results with an empirical example, where we analyse the inflation rate in Germany
for the period 1986–2017.
of persistence is characterized as the order of integration of a strongly dependent
process. To avoid the risk of incorrectly specifying the data generating process
we employ local Whittle estimates which uses only frequencies local to zero. The
limit distribution of the test statistic under the null is not standard but it is well
known in the literature. A Monte Carlo study shows that this inference procedure
performs well in finite samples. We demonstrate the practical utility of these
results with an empirical example, where we analyse the inflation rate in Germany
for the period 1986–2017.
| Original language | English |
|---|---|
| Pages (from-to) | 693–706 |
| Number of pages | 14 |
| Journal | Journal of Time Series Analysis |
| Volume | 40 |
| Issue number | 5 |
| Early online date | 5 Feb 2019 |
| DOIs | |
| Publication status | Published - Sept 2019 |
Bibliographical note
© 2019 John Wiley & Sons Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.Keywords
- Long memory
- break
- local Whittle estimate
- persistence