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
Residual-based Inference for Common Nonlinear Features
Dalarna University, School of Technology and Business Studies, Statistics.
2013 (English)Report (Other academic)
Abstract [en]

This paper investigates common nonlinear features in multivariate nonlinear autore-gressive models via testing the estimated residuals. A Wald-type test is proposed and itis asymptotically Chi-squared distributed. Simulation studies are given to examine thefinite-sample properties of the proposed test.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna , 2013.
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2013:09
Keyword [en]
Common features, Nonlinearity, Residual-based test
National Category
Probability Theory and Statistics
Research subject
Komplexa system - mikrodataanalys
Identifiers
URN: urn:nbn:se:du-11872OAI: oai:DiVA.org:du-11872DiVA: diva2:606645
Available from: 2013-02-20 Created: 2013-02-20 Last updated: 2013-11-11
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 these area.

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 Ftypetest 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
Öerbro: Örebro University, 2013
Series
Örebro Studies in Statistics, ISSN 1651-8608 ; 6
Keyword
Nonlinearity, Time series, Econometrics, Smooth transition, Common features, Cointegration, Forecasting, Residual-based, PPP.
National Category
Probability Theory and Statistics
Research subject
Komplexa system - mikrodataanalys
Identifiers
urn:nbn:se:du-13253 (URN)978-91-7668-952-3 (ISBN)
Public defence
2013-10-01, Långhuset HSL 3, Örebro University, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2013-11-11 Created: 2013-11-11 Last updated: 2015-03-18Bibliographically approved

Open Access in DiVA

fulltext(198 kB)63 downloads
File information
File name FULLTEXT02.pdfFile size 198 kBChecksum SHA-512
d33aacd5a1c844faf2f69078fcb12617c827ed3187e50e27b1ce98119ae427dd3303bebf7e16a981ba539556026411e7382f80d203b2d4b8be3bc9aa47fd099e
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Li, Dao
By organisation
Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 102 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 725 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