Time-Varying Smooth Transition Autoregressive Models
Bibliographic Data
| ID | 19418402 |
|---|---|
| Authors | Stefan Lundbergh (Stockholm School of Economics), Timo Teräsvirta (0000-0002-0947-2114, Stockholm School of Economics), Dick Van Dijk (0000-0002-1880-204X, Erasmus University Rotterdam) |
| Year | 2003 |
| Volume | 21 |
| Issue | 1 |
| Pages | 104-121 |
| Publication date | 2003-01-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1198/073500102288618810 |
| OpenAlex | W1966691067 |
| Language | EN |
| Citations received | 10 |
| References cited | 36 |
Nonlinear regime-switching behavior and structural change are often perceived as competing alternatives to linearity. In this article we study the so-called time-varying smooth transition autoregressive (TV-STAR) model, which can be used both for describing simultaneous nonlinearity and structural change and for distinguishing between these features. Two modeling strategies for empirical specification of TV-STAR models are developed. Monte Carlo simulations show that neither of the two strategies dominates the other. A specific-to-general-to-specific procedure is best suited for obtaining a first impression of the importance of nonlinearity and/or structural change for a particular time series. A specific-to-general procedure is most useful in careful specification of a model with nonlinear and/or time-varying properties. An empirical application to a large dataset of U.S. macroeconomic time series illustrates the relative merits of both modeling strategies
Autoregressive integrated moving average · Autoregressive model · Econometrics · Monte Carlo method · Nonlinear system · Specification · STAR model · Statistics · Time series · Computer Science · Economic theories and models · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact
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| Citations per year | 0.45 |
|---|---|
| Citation span | 2004 - 2025 (22) |
| Highly cited | No |