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University of Illinois at Urbana-Champaign

A Study on Locally Persistent Time Series

Abstract

dc:description

While it is recognized that many economic time series are highly persistent over certain ranges, less persistent results are also found around very long horizons, indicating the existence of local or temporary persistency. Seeking to describe the dynamics of locally persistent processes, this thesis uses a block local-to-unity model. A test for stationarity against locally persistency is studied. An empirical application using US time series of real GNP, real interest rates and real exchange rates illustrates the importance of this class of processes and tests for applied works. It is also studied co-movement of time series with local persistency. In particular, a residual based test for the null hypothesis of co-movement between two processes with local persistency is proposed. With this new technique, one fills in an existing lacuna in econometrics, in which long-run relationships can also be studied if the dependent and independent variables do not have a unit root, but do exhibit local persistency. The thesis is finalized by applying the proposed test to study the Fisher effect in the determination of real interest rate.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Economics
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • De Oliveira Lima, Luiz Renato Regis
Contributors dc:contributor
  • Zhijie Xiao

Subjects

dc:subject × 1

Rights

Language dc:language
eng

Identifiers

dc:identifier.*
Identifier
(MiAaPQ)AAI3086043
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/85532

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

De Oliveira Lima, Luiz Renato Regis. A Study on Locally Persistent Time Series. Dissertation thesis, University of Illinois at Urbana-Champaign, 2015. http://hdl.handle.net/2142/85532