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Original Articles

Bimodal Birnbaum–Saunders generalized autoregressive score model

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Pages 2585-2606 | Received 11 Aug 2017, Accepted 12 Jan 2018, Published online: 27 Jan 2018

References

  • P. Andres and A. Harvey, The dynamic location/scale model: With applications to intra-day financial data, Cambridge Economic Working Paper Series, 2012.
  • M. Barros, G.A. Paula, and V. Leiva, A new class of survival regression models with heavy-tailed errors: Robustness and diagnostics, Lifetime Data Anal. 14 (2008), pp. 316–332. doi: 10.1007/s10985-008-9085-1
  • L. Bauwens and P. Giot, The logarithmic ACD model: An application to the bid-ask quote process of three NYSE stocks, Ann. Économ. Statist. 60 (2000), pp. 117–149.
  • M.A. Benjamin, R.A. Rigby, and D.M. Stasinopoulos, Generalized autoregressive moving average models, J. Amer. Statist. Assoc. 98 (2003), pp. 214–223. doi: 10.1198/016214503388619238
  • C.R. Bhatti, The Birnbaum–Saunders autoregressive conditional duration model, Math. Comput. Simulation 80 (2010), pp. 2062–2078. doi: 10.1016/j.matcom.2010.01.011
  • Z.W. Birnbaum and S.C. Saunders, A new family of life distributions, J. Appl. Probab. 6 (1969), pp. 319–327. doi: 10.1017/S0021900200032848
  • F. Blasques, S.J. Koopman, K. Łasak, and A. Lucas, In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models, Int. J. Forecast. 32 (2016), pp. 875–887. doi: 10.1016/j.ijforecast.2015.11.018
  • F. Blasques, S.J. Koopman, and A. Lucas, Maximum likelihood estimation for generalized autoregressive score models, Tinbergen Institute Discussion Paper, 2014.
  • F. Blasques, S.J. Koopman, and A. Lucas, Stationarity and ergodicity of univariate generalized autoregressive score processes, Electron. J. Stat. 8 (2014), pp. 1088–1112. doi: 10.1214/14-EJS924
  • T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, J. Econom. 31 (1986), pp. 307–327. doi: 10.1016/0304-4076(86)90063-1
  • D.R. Cox, Statistical analysis of time series: Some recent developments, Scand. J. Statist. 8 (1981), pp. 93–115.
  • D. Creal, S.J. Koopman, and A. Lucas, A general framework for observation driven time-varying parameter models, Tinbergen Institute Discussion paper, 2008.
  • D. Creal, S.J. Koopman, and A. Lucas, Generalized autoregressive score models with applications, J. Appl. Econometrics 28 (2013), pp. 777–795. doi: 10.1002/jae.1279
  • J.A. Díaz-García and J.R. Domínguez-Molina, Some generalisations of Birnbaum–Saunders and sinh-normal distributions, Int. Math. Forum 1 (2006), pp. 1709–1727. doi: 10.12988/imf.2006.06146
  • J.A. Doornik, An Object-Oriented Matrix Programming Language Ox 6, Timberlake Consultants Press, London, 2009.
  • R. Engle, New frontiers for ARCH models, J. Appl. Econom. 17 (2002), pp. 425–446. doi: 10.1002/jae.683
  • R.F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50 (1982), pp. 987–1007. doi: 10.2307/1912773
  • R.F. Engle and J.R. Russell, Autoregressive conditional duration: A new model for irregularly spaced transaction data, Econometrica 66 (1998), pp. 1127–1162. doi: 10.2307/2999632
  • G. Fiorentini, G. Calzolari, and L. Panattoni, Analytic derivatives and the computation of GARCH estimates, J. Appl. Econom. 11 (1996), pp. 399–417. doi: 10.1002/(SICI)1099-1255(199607)11:4<399::AID-JAE401>3.0.CO;2-R
  • M. Galea, V. Leiva-Sánchez, and G. Paula, Influence diagnostics in log-Birnbaum–Saunders regression models, J. Appl. Stat. 31 (2004), pp. 1049–1064. doi: 10.1080/0266476042000280409
  • V. Leiva, The Birnbaum–Saunders Distribution, Academic Press, London, 2015.
  • V. Leiva, E. Athayde, C. Azevedo, and C. Marchant, Modeling wind energy flux by a Birnbaum–Saunders distribution with an unknown shift parameter, J. Appl. Stat. 38 (2011), pp. 2819–2838. doi: 10.1080/02664763.2011.570319
  • V. Leiva, C. Marchant, H. Saulo, M. Aslam, and F. Rojas, Capability indices for Birnbaum–Saunders processes applied to electronic and food industries, J. Appl. Stat. 41 (2014), pp. 1881–1902. doi: 10.1080/02664763.2014.897690
  • V. Leiva, H. Saulo, J. Leão, and C. Marchant, A family of autoregressive conditional duration models applied to financial data, Comput. Statist. Data Anal. 79 (2014), pp. 175–191. doi: 10.1016/j.csda.2014.05.016
  • A.J. Lemonte and G.M. Cordeiro, Birnbaum–Saunders nonlinear regression models, Comput. Statist. Data Anal. 53 (2009), pp. 4441–4452. doi: 10.1016/j.csda.2009.06.015
  • A.J. Lemonte and A.G. Patriota, Influence diagnostics in Birnbaum–Saunders nonlinear regression models, J. Appl. Stat. 38 (2011), pp. 871–884. doi: 10.1080/02664761003692357
  • J.W. Lin and A.I. McLeod, Improved Peña–Rodriguez portmanteau test, Comput. Statist. Data Anal. 51 (2006), pp. 1731–1738. doi: 10.1016/j.csda.2006.06.010
  • G.M. Ljung and G.E. Box, On a measure of lack of fit in time series models, Biometrika 65 (1978), pp. 297–303. doi: 10.1093/biomet/65.2.297
  • C. Marchant, V. Leiva, F.J.A. Cysneiros, and J.F. Vivanco, Diagnostics in multivariate generalized Birnbaum–Saunders regression models, J. Appl. Stat. 43 (2016), pp. 2829–2849. doi: 10.1080/02664763.2016.1148671
  • G.G. Matos, Modelos GAS Aplicados a Séries Temporais de Vazão e Vento, Master's thesis, Pontifícia Universidade Católica do Rio de Janeiro, 2013.
  • A.C. Monti, A proposal for a residual autocorrelation test in linear models, Biometrika 81 (1994), pp. 776–780. doi: 10.1093/biomet/81.4.776
  • W.J. Owen and H.K.T. Ng, Revisit of relationships and models for the Birnbaum–Saunders and inverse-Gaussian distributions, J. Stat. Distrib. Appl. 2 (2015), pp. 1–23. doi: 10.1186/s40488-015-0034-8
  • L. Pascual, J. Romo, and E. Ruiz, Bootstrap prediction for returns and volatilities in GARCH models, Comput. Statist. Data Anal. 50 (2006), pp. 2293–2312. doi: 10.1016/j.csda.2004.12.008
  • R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2015. Available at https://www.R-project.org/.
  • J.R. Rieck and J.R. Nedelman, A log-linear model for the Birnbaum–Saunders distribution, Technometrics 33 (1991), pp. 51–60.
  • H. Saulo, J. Leão, V. Leiva, and R.G. Aykroyd, Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data, Statist. Papers (2017), in press. doi: 10.1007/s00362-017-0888-6..
  • R.H. Shumway and D.S. Stoffer, Time Series Analysis and Its Applications: With R Examples, Springer, New York, 2011.
  • R.S. Tsay, Analysis of Financial Time Series, John Wiley & Sons, New York, 2002.

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