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Large-sample inference for nonparametric regression with dependent errors

Citations

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Cited by:

  1. Gao, Jiti & Linton, Oliver & Peng, Bin, 2020. "Inference On A Semiparametric Model With Global Power Law And Local Nonparametric Trends," Econometric Theory, Cambridge University Press, vol. 36(2), pages 223-249, April.
  2. Hidalgo, J., 2008. "Specification testing for regression models with dependent data," Journal of Econometrics, Elsevier, vol. 143(1), pages 143-165, March.
  3. Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, May.
  4. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
  5. Hassler, U. & Marmol, F. & Velasco, C., 2006. "Residual log-periodogram inference for long-run relationships," Journal of Econometrics, Elsevier, vol. 130(1), pages 165-207, January.
  6. Peter M Robinson, 2004. "ROBUST COVARIANCE MATRIX ESTIMATION: "HAC" Estimates with Long Memory/Antipersistence Correction," STICERD - Econometrics Paper Series 471, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  7. Guerre, 2004. "Design-Adaptive Pointwise Nonparametric Regression Estimation For Recurrent Markov Time Series," Econometrics 0411007, University Library of Munich, Germany.
  8. Javier Hidalgo, 2007. "Specification Testing Forregression Models Withdependent Data," STICERD - Econometrics Paper Series 518, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  9. Robinson, Peter M., 2004. "Robust covariance matrix estimation : HAC estimates with long memory/antipersistence correction," LSE Research Online Documents on Economics 2157, London School of Economics and Political Science, LSE Library.
  10. Cai, Zongwu & Ren, Yu & Yang, Bingduo, 2015. "A semiparametric conditional capital asset pricing model," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 117-126.
  11. repec:ipg:wpaper:2014-066 is not listed on IDEAS
  12. Hidalgo, Javier, 2007. "Specification testing for regression models with dependent data," LSE Research Online Documents on Economics 6799, London School of Economics and Political Science, LSE Library.
  13. Morten Ø. Nielsen & Per Houmann Frederiksen, 2008. "Fully Modified Narrow-band Least Squares Estimation Of Stationary Fractional Cointegration," Working Paper 1171, Economics Department, Queen's University.
  14. Tanujit Dey & Kun Ho Kim & Chae Young Lim, 2018. "Bayesian time series regression with nonparametric modeling of autocorrelation," Computational Statistics, Springer, vol. 33(4), pages 1715-1731, December.
  15. Robinson, Peter M., 2012. "Nonparametric trending regression with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 4-14.
  16. Hidalgo, Javier, 2021. "Bootstrap long memory processes in the frequency domain," LSE Research Online Documents on Economics 106149, London School of Economics and Political Science, LSE Library.
  17. Karlsen, Hans Arnfinn & Tjostheim, Dag, 1998. "Nonparametric estimation in null recurrent times series," SFB 373 Discussion Papers 1998,50, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  18. Violetta Dalla & Javier Hidalgo, 2015. "Testing for Breaks in Regression Models with Dependent Data," STICERD - Econometrics Paper Series /2015/584, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  19. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
  20. Florence Merlevède & Magda Peligrad, 2010. "Moderate Deviations for Linear Processes Generated by Martingale-Like Random Variables," Journal of Theoretical Probability, Springer, vol. 23(1), pages 277-300, March.
  21. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
  22. Youndjé, É. & Vieu, P., 2006. "A note on quantile estimation for long-range dependent stochastic processes," Statistics & Probability Letters, Elsevier, vol. 76(2), pages 109-116, January.
  23. Robinson, Peter, 2019. "Spatial long memory," LSE Research Online Documents on Economics 102182, London School of Economics and Political Science, LSE Library.
  24. Heni Boubaker & Nadia Sghaier, 2014. "Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach," Working Papers 2014-66, Department of Research, Ipag Business School.
  25. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  26. Jianqing Fan & Yingying Fan & Jinchi Lv, 0. "Aggregation of Nonparametric Estimators for Volatility Matrix," Journal of Financial Econometrics, Oxford University Press, vol. 5(3), pages 321-357.
  27. Toshio Honda, 2013. "Nonparametric quantile regression with heavy-tailed and strongly dependent errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 23-47, February.
  28. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
  29. Hira L. Koul & Fang Li, 2020. "Comparing two nonparametric regression curves in the presence of long memory in covariates and errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(4), pages 499-517, May.
  30. Gao, Jiti & Anh, Vo & Heyde, Chris, 2002. "Statistical estimation of nonstationary Gaussian processes with long-range dependence and intermittency," Stochastic Processes and their Applications, Elsevier, vol. 99(2), pages 295-321, June.
  31. Kris Brabanter & Farzad Sabzikar, 2021. "Asymptotic theory for regression models with fractional local to unity root errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(7), pages 997-1024, October.
  32. Peter M Robinson, 2009. "Inference On Nonparametrically Trending Time Series With Fractional Errors," STICERD - Econometrics Paper Series 532, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  33. Robinson, Peter, 2008. "Inference on nonparametrically trending time series with fractional errors," LSE Research Online Documents on Economics 25471, London School of Economics and Political Science, LSE Library.
  34. Fabrizio Iacone, 2010. "Local Whittle estimation of the memory parameter in presence of deterministic components," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(1), pages 37-49, January.
  35. Biqing Cai & Chaohua Dong & Jiti Gao, 2015. "Orthogonal Series Estimation in Nonlinear Cointegrating Models with Endogeneity," Monash Econometrics and Business Statistics Working Papers 18/15, Monash University, Department of Econometrics and Business Statistics.
  36. Yuanhua Feng & Thomas Gries, 2017. "Data-driven local polynomial for the trend and its derivatives in economic time series," Working Papers CIE 102, Paderborn University, CIE Center for International Economics.
  37. Violetta Dalla & Liudas Giraitis & Hira L. Koul, 2014. "Studentizing Weighted Sums Of Linear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(2), pages 151-172, March.
  38. Robinson, P.M., 2011. "Asymptotic theory for nonparametric regression with spatial data," Journal of Econometrics, Elsevier, vol. 165(1), pages 5-19.
  39. Linyuan Li & Kewei Lu, 2013. "On rate-optimal nonparametric wavelet regression with long memory moving average errors," Statistical Inference for Stochastic Processes, Springer, vol. 16(2), pages 127-145, July.
  40. Magda Peligrad & Hailin Sang, 2013. "Central Limit Theorem for Linear Processes with Infinite Variance," Journal of Theoretical Probability, Springer, vol. 26(1), pages 222-239, March.
  41. Boubaker, Heni & Sghaier, Nadia, 2015. "Semiparametric generalized long-memory modeling of some mena stock market returns: A wavelet approach," Economic Modelling, Elsevier, vol. 50(C), pages 254-265.
  42. 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.
  43. 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.
  44. Dedecker, Jérôme & Merlevède, Florence, 2011. "Rates of convergence in the central limit theorem for linear statistics of martingale differences," Stochastic Processes and their Applications, Elsevier, vol. 121(5), pages 1013-1043, May.
  45. Fan, Jianqing & Fan, Yingying & Jiang, Jiancheng, 2007. "Dynamic Integration of Time- and State-Domain Methods for Volatility Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 618-631, June.
  46. George Kapetanios & Zacharias Psaradakis, 2016. "Semiparametric Sieve-Type Generalized Least Squares Inference," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 951-985, June.
  47. Dalla, Violetta & Giraitis, Liudas & Robinson, Peter M., 2020. "Asymptotic theory for time series with changing mean and variance," Journal of Econometrics, Elsevier, vol. 219(2), pages 281-313.
  48. Lihong Wang, 2020. "Lack of fit test for long memory regression models," Statistical Papers, Springer, vol. 61(3), pages 1043-1067, June.
  49. Robinson, Peter, 2008. "Developments in the analysis of spatial data," LSE Research Online Documents on Economics 25473, London School of Economics and Political Science, LSE Library.
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