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Testing common nonlinear features in vector nonlinear autoregressive models
Dalarna University, School of Technology and Business Studies, Statistics.
Dalarna University, School of Technology and Business Studies, Statistics.
2012 (English)Report (Other academic)
Abstract [en]

This paper studies a special class of vector smooth-transition autoregressive (VSTAR) models that contains common nonlinear features (CNFs), for which we proposed a triangular representation and developed a procedure of testing CNFs in a VSTAR model. We first test a unit root against a stable STAR process for each individual time series and then examine whether CNFs exist in the system by Lagrange Multiplier (LM) test if unit root is rejected in the first step. The LM test has standard Chi-squared asymptotic distribution. The critical values of our unit root tests and small-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 of United States (1985:1 to 2011:11).

Place, publisher, year, edition, pages
2012.
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2012:4
National Category
Probability Theory and Statistics
Research subject
Komplexa system - mikrodataanalys
Identifiers
URN: urn:nbn:se:du-11114OAI: oai:DiVA.org:du-11114DiVA: diva2:562683
Available from: 2012-10-30 Created: 2012-10-25 Last updated: 2013-11-11Bibliographically 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 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

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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More styles
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  • Other locale
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Output format
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