IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/66509.html
   My bibliography  Save this paper

Inference on nonstationary time series with moving mean

Author

Listed:
  • Gao, Jiti
  • Robinson, Peter M.

Abstract

A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.

Suggested Citation

  • Gao, Jiti & Robinson, Peter M., 2014. "Inference on nonstationary time series with moving mean," LSE Research Online Documents on Economics 66509, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:66509
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/66509/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Donggyu Sul, 2007. "Transition Modeling and Econometric Convergence Tests," Econometrica, Econometric Society, vol. 75(6), pages 1771-1855, November.
    2. Cătălin Stărică & Clive Granger, 2005. "Nonstationarities in Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 503-522, August.
    3. Zhibiao Zhao & Yiyun Zhang & Runze Li, 2014. "Non-Parametric Estimation Under Strong Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 4-15, January.
    4. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
    5. Roussas, George G. & Tran, Lanh T. & Ioannides, D. A., 1992. "Fixed design regression for time series: Asymptotic normality," Journal of Multivariate Analysis, Elsevier, vol. 40(2), pages 262-291, February.
    6. Phillips, Peter C.B., 2007. "Regression With Slowly Varying Regressors And Nonlinear Trends," Econometric Theory, Cambridge University Press, vol. 23(4), pages 557-614, August.
    7. Deo, R. S., 1997. "Nonparametric regression with long-memory errors," Statistics & Probability Letters, Elsevier, vol. 33(1), pages 89-94, April.
    8. Robinson, Peter M., 2012. "Nonparametric trending regression with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 4-14.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunus Emre Ergemen, 2016. "Generalized Efficient Inference on Factor Models with Long-Range Dependence," CREATES Research Papers 2016-05, Department of Economics and Business Economics, Aarhus University.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Jiti & Robinson, Peter M., 2016. "Inference On Nonstationary Time Series With Moving Mean," Econometric Theory, Cambridge University Press, vol. 32(2), pages 431-457, April.
    2. Yonghui Zhang & Liangjun Su & Peter C. B. Phillips, 2012. "Testing for common trends in semi‐parametric panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 56-100, February.
    3. Kong, Jianning & Phillips, Peter C.B. & Sul, Donggyu, 2019. "Weak σ-convergence: Theory and applications," Journal of Econometrics, Elsevier, vol. 209(2), pages 185-207.
    4. Uematsu, Yoshimasa, 2016. "Asymptotic efficiency of the OLS estimator with singular limiting sample moment matrices," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 104-110.
    5. Robinson, Peter M., 1997. "Large-sample inference for nonparametric regression with dependent errors," LSE Research Online Documents on Economics 302, London School of Economics and Political Science, LSE Library.
    6. Ke Zhu, 2018. "Statistical inference for autoregressive models under heteroscedasticity of unknown form," Papers 1804.02348, arXiv.org, revised Aug 2018.
    7. Chen, Zhihong & Xia, Huizhu, 2020. "Trend instrumental variable regression with an application to the US New Keynesian Phillips Curve," Economic Modelling, Elsevier, vol. 93(C), pages 595-604.
    8. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    9. Raymond Li, Roselyne Joyeux, and Ronald D. Ripple, 2014. "International Natural Gas market Integration," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    10. Smirnykh, Larisa & Woergoetter, Andreas, 2021. "Regional convergence in CEE before and after the Global Financial Crisis," IHS Working Paper Series 33, Institute for Advanced Studies.
    11. Kazemzadeh, Emad & Fuinhas, José Alberto & Koengkan, Matheus & Shadmehri, Mohammad Taher Ahmadi, 2023. "Relationship between the share of renewable electricity consumption, economic complexity, financial development, and oil prices: A two-step club convergence and PVAR model approach," International Economics, Elsevier, vol. 173(C), pages 260-275.
    12. Christopoulos, Konstantinos & Eleftheriou, Konstantinos, 2020. "Premature mortality in the US: A convergence study," Social Science & Medicine, Elsevier, vol. 258(C).
    13. Barunik, Jozef & Krehlik, Tomas, 2016. "Measuring the frequency dynamics of financial and macroeconomic connectedness," FinMaP-Working Papers 54, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    14. Tirtha Chaterjee & A. Ganesh Kumar, 2014. "Neighborhood and Agricultural Clusters across States of India," Working Papers id:6272, eSocialSciences.
    15. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    16. Ewing, Bradley T. & Malik, Farooq, 2016. "Volatility spillovers between oil prices and the stock market under structural breaks," Global Finance Journal, Elsevier, vol. 29(C), pages 12-23.
    17. Xuejun Wang & Yi Wu & Shuhe Hu, 2016. "Exponential probability inequality for $$m$$ m -END random variables and its applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(2), pages 127-147, February.
    18. Hu, Zhishui & Phillips, Peter C.B. & Wang, Qiying, 2021. "Nonlinear Cointegrating Power Function Regression With Endogeneity," Econometric Theory, Cambridge University Press, vol. 37(6), pages 1173-1213, December.
    19. Sulekha Hembram & Souparna Maji & Sushil Kr. Haldar, 2019. "Club Convergence among the Major Indian States During 1982–2014: Does Investment in Human Capital Matter?," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 20(2), pages 184-204, September.
    20. K.P. Gluschenko (glu@nsu.ru ), 2010. "Income inequality in Russian regions: comparative analysis," Journal "Region: Economics and Sociology", Institute of Economics and Industrial Engineering of Siberian Branch of RAS, vol. 4.

    More about this item

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:66509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.