oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Testing common nonlinear features in vector nonlinear autoregressive models
Örebro University, Örebro University School of Business. School of Technology and Business Studies, Dalarna University, Borlänge,Sweden.
School of Technology and Business Studies, Dalarna University, Borlänge,Sweden.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models, which contains common nonlinear features (CNFs). We proposed a triangular representation for these models and developed a procedure for testing CNFs in a VSTAR model. We first test a unit root against a stationary STAR process for each individual time series and subsequently examine whether CNFs exist in the system with the Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has a standard Chi-square asymptotic distribution. The critical values of our unit root tests and finite-sample properties of the F form of our LM test are studied by Monte Carlo simulations. We illustrate how to test and model CNFs using the monthly growth of consumption and income data for the United States (1985:1 to 2011:11). 

Keywords [en]
Common features, Lagrange Multiplier test, Vector STAR models
National Category
Mathematics
Research subject
Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-32409OAI: oai:DiVA.org:oru-32409DiVA, id: diva2:664446
Available from: 2013-11-15 Created: 2013-11-15 Last updated: 2017-10-17Bibliographically approved
In thesis
1. Common features in vector nonlinear time series models
Open this publication in new window or tab >>Common features in vector nonlinear time series models
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in thesearea.

Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified.

The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An F-type test for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail, and thereafter illustrated within two corresponding macroeconomic data sets.

Place, publisher, year, edition, pages
Örebro: Örebro universitet, 2013. p. 27
Series
Örebro Studies in Statistics, ISSN 1651-8608 ; 6
Keywords
nonliearity, time series, econometrics, smooth transition, common features, cointegration, forecasting, residual-based, ppp
National Category
Mathematics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-32233 (URN)978-91-7668-952-3 (ISBN)
Public defence
2013-10-01, Hörsal 3, Långhuset, Örebro universitet, Fakultetsgatan 1, 701 82 Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2013-11-15 Created: 2013-11-04 Last updated: 2017-10-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

By organisation
Örebro University School of Business
Mathematics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 23 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf