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Specification, Estimation and Evaluation of Vector Smooth Transition Autoregressive Models with Applications

Author

Listed:
  • Timo Teräsvirta

    () (Aarhus University and CREATES)

  • Yukai Yang

    () (CORE, Université catholique de Louvain and CREATES)

Abstract

We consider a nonlinear vector model called the logistic vector smooth transition autoregressive model. The bivariate single-transition vector smooth transition regression model of Camacho (2004) is generalised to a multivariate and multitransition one. A modelling strategy consisting of specification, including testing linearity, estimation and evaluation of these models is constructed. Nonlinear least squares estimation of the parameters of the model is discussed. Evaluation by misspecification tests is carried out using tests derived in a companion paper. The use of the modelling strategy is illustrated by two applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible asymmetries in it considered. The second application consists of modelling two well known Icelandic riverflow series, previously considered by many hydrologists and time series analysts. JEL Classification: C32, C51, C52

Suggested Citation

  • Timo Teräsvirta & Yukai Yang, 2014. "Specification, Estimation and Evaluation of Vector Smooth Transition Autoregressive Models with Applications," CREATES Research Papers 2014-08, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2014-08
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    References listed on IDEAS

    as
    1. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    2. Watson, Mark W & Engle, Robert F, 1985. "Testing for Regression Coefficient Stability with a Stationary AR(1) Alternative," The Review of Economics and Statistics, MIT Press, vol. 67(2), pages 341-346, May.
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    Cited by:

    1. Schleer, Frauke, 2013. "Finding starting-values for maximum likelihood estimation of vector STAR models," ZEW Discussion Papers 13-076, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    2. Arkady Gevorkyan & Willi Semmler, 2016. "Macroeconomic variables and the sovereign risk premia in EMU, non-EMU EU, and developed countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(1), pages 1-35, February.
    3. Caggiano, Giovanni & Castelnuovo, Efrem & Groshenny, Nicolas, 2014. "Uncertainty shocks and unemployment dynamics in U.S. recessions," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 78-92.
    4. Fredj Jawadi & Georges Prat, 2017. "Equity prices and fundamentals: a DDM–APT mixed approach," Review of Quantitative Finance and Accounting, Springer, vol. 49(3), pages 661-695, October.
    5. Timo Teräsvirta & Yukai Yang, 2014. "Linearity and Misspecification Tests for Vector Smooth Transition Regression Models," CREATES Research Papers 2014-04, Department of Economics and Business Economics, Aarhus University.
    6. YANG, Yukai, 2014. "Testing constancy of the error covariance matrix in vector models against parametric alternatives using a spectral decomposition," CORE Discussion Papers 2014017, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. repec:eee:wdevel:v:96:y:2017:i:c:p:490-502 is not listed on IDEAS
    8. Donayre, Luiggi & Panovska, Irina, 2016. "State-dependent exchange rate pass-through behavior," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 170-195.
    9. Schleer, Frauke & Semmler, Willi, 2015. "Financial sector and output dynamics in the euro area: Non-linearities reconsidered," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 235-263.
    10. Balcilar, Mehmet & Thompson, Kirsten & Gupta, Rangan & van Eyden, Reneé, 2016. "Testing the asymmetric effects of financial conditions in South Africa: A nonlinear vector autoregression approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 30-43.
    11. Jawadi Fredj & Mallick Sushanta K. & Sousa Ricardo M., 2014. "Fiscal policy in the BRICs," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(2), pages 1-15, April.
    12. repec:eme:isetez:s1571-038620150000024018 is not listed on IDEAS
    13. Frauke Schleer, 2015. "Finding Starting-Values for the Estimation of Vector STAR Models," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-26, January.
    14. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    15. Schleer, Frauke & Semmler, Willi, 2013. "Financial sector-output dynamics in the euro area: Non-linearities reconsidered," ZEW Discussion Papers 13-068, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
    16. repec:eee:ecmode:v:68:y:2018:i:c:p:273-292 is not listed on IDEAS
    17. Svetlana Vtyurina & Zulima Leal, 2016. "Fiscal Multipliers and Institutions in Peru; Getting the Largest Bang for the Sol," IMF Working Papers 16/144, International Monetary Fund.
    18. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    19. Willi Semmler & Christian R. Proaño, 2015. "Escape Routes from Sovereign Default Risk in the Euro Area," International Symposia in Economic Theory and Econometrics,in: Monetary Policy in the Context of the Financial Crisis: New Challenges and Lessons, volume 24, pages 163-193 Emerald Publishing Ltd.
    20. Luiggi Donayre & Neil A. Wilmot, 2016. "The Asymmetric Effects of Oil Price Shocks on the Canadian Economy," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 167-182.

    More about this item

    Keywords

    Vector STAR model; Modelling nonlinearity; Vector autoregression; Generalized impulse response; Asymmetry; Oil price River flow;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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