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Data tilting for time series

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  • Peter Hall
  • Qiwei Yao

Abstract

Summary. We develop a general methodology for tilting time series data. Attention is focused on a large class of regression problems, where errors are expressed through autoregressive processes. The class has a range of important applications and in the context of our work may be used to illustrate the application of tilting methods to interval estimation in regression, robust statistical inference and estimation subject to constraints. The method can be viewed as ‘empirical likelihood with nuisance parameters’.

Suggested Citation

  • Peter Hall & Qiwei Yao, 2003. "Data tilting for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 425-442, May.
    • Handle: RePEc:bla:jorssb:v:65:y:2003:i:2:p:425-442
    DOI: 10.1111/1467-9868.00394
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    Cited by:

    1. Marshall, Ben R. & Cahan, Rochester H., 2005. "Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient?," Research in International Business and Finance, Elsevier, vol. 19(3), pages 384-398, September.
    2. Tursunalieva, Ainura & Silvapulle, Param, 2016. "Nonparametric estimation of operational value-at-risk (OpVaR)," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 194-201.
    3. Peng, Liang & Einmahl, John, 2021. "Improved regression inference using a second overlapping regression model," Other publications TiSEM c529c2b9-0eee-440e-b015-8, Tilburg University, School of Economics and Management.
    4. Santiago Gamba-Santamaria & Oscar Fernando Jaulin-Mendez & Luis Fernando Melo-Velandia & Carlos Andrés Quicazán-Moreno, 2016. "Comparison of methods for estimating the uncertainty of value at risk," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 33(4), pages 595-624, October.
    5. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Santiago Gamba Santamaría & Oscar Fernando Jaulín Méndez & Luis Fernando Melo Velandia & Carlos Andrés Quicazán Moreno, 2015. "Comparación De Métodos Para La Estimación De La Incertidumbre Del Valor En Riesgo," Temas de Estabilidad Financiera 83, Banco de la Republica de Colombia.
    7. Nieto, María Rosa, 2010. "Bootstrap prediction intervals for VaR and ES in the context of GARCH models," DES - Working Papers. Statistics and Econometrics. WS ws102814, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Peng, Liang & Einmahl, John, 2021. "Improved regression inference using a second overlapping regression model," Discussion Paper 2021-029, Tilburg University, Center for Economic Research.
    9. Li, Minqiang & Peng, Liang & Qi, Yongcheng, 2011. "Reduce computation in profile empirical likelihood method," MPRA Paper 33744, University Library of Munich, Germany.
    10. Marc G. Genton & Peter Hall, 2016. "A tilting approach to ranking influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 77-97, January.
    11. Ainura Tursunalieva & Param Silvapulle, 2013. "Non-parametric Estimation of Operational Risk and Expected Shortfall," Monash Econometrics and Business Statistics Working Papers 23/13, Monash University, Department of Econometrics and Business Statistics.
    12. Peter Hall & D. M. Titterington & Jing‐Hao Xue, 2009. "Tilting methods for assessing the influence of components in a classifier," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 783-803, September.

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