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A note on the corrected Akaike information criterion for threshold autoregressive models

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  • C. S. Wong
  • W. K. Li

Abstract

A bias‐corrected Akaike information criterion AICC is derived for self‐exciting threshold autoregressive (SETAR) models. The small sample properties of the Akaike information criteria (AIC, AICC) and the Bayesian information criterion (BIC) are studied using simulation experiments. It is suggested that AICC performs much better than AIC and BIC in small samples and should be put in routine usage.

Suggested Citation

  • C. S. Wong & W. K. Li, 1998. "A note on the corrected Akaike information criterion for threshold autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 113-124, January.
    • Handle: RePEc:bla:jtsera:v:19:y:1998:i:1:p:113-124
    DOI: 10.1111/1467-9892.00080
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    Cited by:

    1. Mittnik, Stefan & Semmler, Willi, 2013. "The real consequences of financial stress," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1479-1499.
    2. Edward P. Campbell, 2004. "Bayesian selection of threshold autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(4), pages 467-482, July.
    3. Francesco Giordano & Marcella Niglio & Cosimo Damiano Vitale, 2023. "Linear approximation of the Threshold AutoRegressive model: an application to order estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 27-56, March.
    4. Metiu, Norbert & Hilberg, Björn & Grill, Michael, 2016. "Credit constraints and the international propagation of US financial shocks," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 67-80.
    5. Galeano, Pedro & Peña, Daniel, 2004. "Model selection criteria and quadratic discrimination in ARMA and SETAR time series models," DES - Working Papers. Statistics and Econometrics. WS ws041406, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Eugene Canjels & Gauri Prakash-Canjels & Alan M. Taylor, 2004. "Measuring Market Integration: Foreign Exchange Arbitrage and the Gold Standard, 1879-1913," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 868-882, November.
    7. Pitarakis Jean-Yves, 2006. "Model Selection Uncertainty and Detection of Threshold Effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(1), pages 1-30, March.
    8. Marianna Mitratza & Anton E. Kunst & Jan W. P. F. Kardaun, 2019. "Detecting Mortality Trends in the Netherlands Across 625 Causes of Death," IJERPH, MDPI, vol. 16(21), pages 1-9, October.
    9. Rinke Saskia & Sibbertsen Philipp, 2016. "Information criteria for nonlinear time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 325-341, June.
    10. Metiu, Norbert & Hilberg, Björn & Grill, Michael, 2015. "Financial frictions and global spillovers," Discussion Papers 04/2015, Deutsche Bundesbank.
    11. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2004. "Estimating threshold subset autoregressive moving-average models by genetic algorithms," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 39-61.
    12. Brian Y. An & Adam Butz & Min-Kyeong Cha & Joshua L. Mitchell, 2023. "Following neighbors or regional leaders? Unpacking the effect of geographic proximity in local climate policy diffusion," Policy Sciences, Springer;Society of Policy Sciences, vol. 56(4), pages 825-868, December.

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