IDEAS home Printed from https://ideas.repec.org/r/eee/econom/v81y1997i2p281-317.html
   My bibliography  Save this item

Subsampling for heteroskedastic time series

Citations

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


Cited by:

  1. Óscar Arce & Sergio Mayordomo & Juan Ignacio Peña, 2012. "Credit-valuation in the sovereing CDS and bonds markets: Evidence from the euro area crisis," CNMV Working Papers CNMV Working Papers no. 5, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
  2. Geert Bekaert & Robert J. Hodrick, 2001. "Expectations Hypotheses Tests," Journal of Finance, American Finance Association, vol. 56(4), pages 1357-1394, August.
  3. Eduardo Lima & Benjamin Tabak, 2009. "Tests of Random Walk: A Comparison of Bootstrap Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 365-382, November.
  4. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
  5. Sibanjan Mishra, 2019. "Testing Martingale Hypothesis Using Variance Ratio Tests: Evidence from High-frequency Data of NCDEX Soya Bean Futures," Global Business Review, International Management Institute, vol. 20(6), pages 1407-1422, December.
  6. Zhang, Tao & Zhou, Hongfeng & Li, Larry & Gu, Feng, 2015. "Optimal rebalance rules for the constant proportion portfolio insurance strategy – Evidence from China," Economic Systems, Elsevier, vol. 39(3), pages 413-422.
  7. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
  8. Charles, Amélie & Darné, Olivier, 2009. "The random walk hypothesis for Chinese stock markets: Evidence from variance ratio tests," Economic Systems, Elsevier, vol. 33(2), pages 117-126, June.
  9. Gonçalves, Sílvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1367-1384, December.
  10. Arce, Oscar & Mayordomo, Sergio & Peña, Juan Ignacio, 2013. "Credit-risk valuation in the sovereign CDS and bonds markets: Evidence from the euro area crisis," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 124-145.
  11. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," LSE Research Online Documents on Economics 107426, London School of Economics and Political Science, LSE Library.
  12. Regis Augusto Ely, 2011. "Returns Predictability and Stock Market Efficiency in Brazil," Brazilian Review of Finance, Brazilian Society of Finance, vol. 9(4), pages 571-584.
  13. Amélie Charles & Olivier Darné, 2009. "Variance‐Ratio Tests Of Random Walk: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 503-527, July.
  14. Dichtl, Hubert & Drobetz, Wolfgang, 2011. "Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1683-1697, July.
  15. Maharaj, E.A., 1999. "A Test for the Difference Parameter of the ARFIMA Model Using the Moving Blocks Bootstrap," Monash Econometrics and Business Statistics Working Papers 11/99, Monash University, Department of Econometrics and Business Statistics.
  16. Felipe Aparicio & Alvaro Escribano & Ana E. Sipols, 2006. "Range Unit‐Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(4), pages 545-576, July.
  17. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.
  18. Shyh-wei Chen, 2009. "Random walks in asian foreign exchange markets:evidence from new multiple variance ratio tests," Economics Bulletin, AccessEcon, vol. 29(2), pages 1296-1307.
  19. Marine Carrasco & Rachidi Kotchoni, 2015. "Adaptive Realized Kernels," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 757-797.
  20. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.
  21. Arvanitis, Stelios & Topaloglou, Nikolas, 2017. "Testing for prospect and Markowitz stochastic dominance efficiency," Journal of Econometrics, Elsevier, vol. 198(2), pages 253-270.
  22. Marcel Brautigam & Marie Kratz, 2020. "The Impact of the Choice of Risk and Dispersion Measure on Procyclicality," Papers 2001.00529, arXiv.org.
  23. Romano, Joseph P. & Wolf, Michael, 2001. "Improved nonparametric confidence intervals in time series regressions," DES - Working Papers. Statistics and Econometrics. WS ws010201, Universidad Carlos III de Madrid. Departamento de Estadística.
  24. Walde, Janette F., 2007. "Valid hypothesis testing in face of spatially dependent data using multi-layer perceptrons and sub-sampling techniques," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2701-2719, February.
  25. G. Geoffrey Booth & Sanders S. Chang, 2017. "Domestic exchange rate determination in Renaissance Florence," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 11(3), pages 405-445, September.
  26. Mayordomo, Sergio & Peña, Juan Ignacio & Romo, Juan, 2009. "Are There Arbitrage Opportunities in Credit Derivatives Markets? A New Test and an Application to the Case of CDS and ASPs," DEE - Working Papers. Business Economics. WB wb096303, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
  27. Romano, Joseph P & Wolf, Michael, 2001. "Subsampling Intervals in Autoregressive Models with Linear Time Trend," Econometrica, Econometric Society, vol. 69(5), pages 1283-1314, September.
  28. Mehmet Caner, 2006. "A lasso type gmm estimator," Working Paper 210, Department of Economics, University of Pittsburgh, revised Jan 2006.
  29. Politis, Dimitris N. & Romano, Joseph P. & Wolf, Michael, 1999. "On the asymptotic theory of subsampling," DES - Working Papers. Statistics and Econometrics. WS 6334, Universidad Carlos III de Madrid. Departamento de Estadística.
  30. Simionescu, Mihaela, 2022. "Stochastic convergence in per capita energy use in the EU-15 countries. The role of economic growth," Applied Energy, Elsevier, vol. 322(C).
  31. Karolyi, G. Andrew & Kho, Bong-Chan, 2004. "Momentum strategies: some bootstrap tests," Journal of Empirical Finance, Elsevier, vol. 11(4), pages 509-536, September.
  32. Khurshid M. Kiani, 2007. "Asymmetric Business Cycle Fluctuations and Contagion Effects in G7 Countries," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 6(3), pages 237-253, December.
  33. Francesco Bravo, 2011. "Comment on: Subsampling weakly dependent time series and application to extremes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 483-486, November.
  34. Kyriacou, Maria, 2014. "Overlapping sub-sampling and invariance to initial conditions," Discussion Paper Series In Economics And Econometrics 1203, Economics Division, School of Social Sciences, University of Southampton.
  35. Ekström, Magnus, 2014. "A general central limit theorem for strong mixing sequences," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 236-238.
  36. Romano, Joseph P. & Wolf, Michael, 1998. "Subsampling confidence intervals for the autoregressive root," DES - Working Papers. Statistics and Econometrics. WS 6268, Universidad Carlos III de Madrid. Departamento de Estadística.
  37. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
  38. Paulo Parente & Richard J. Smith, 2018. "Kernel block bootstrap," CeMMAP working papers CWP48/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  39. Kim, Jae H., 2006. "Wild bootstrapping variance ratio tests," Economics Letters, Elsevier, vol. 92(1), pages 38-43, July.
  40. Franke, Jürgen & Kreiss, Jens-Peter & Mammen, Enno & Neumann, Michael H., 1998. "Properties of the nonparametric autoregressive bootstrap," SFB 373 Discussion Papers 1998,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  41. Peter Hall & Yvonne Pittelkow & Malay Ghosh, 2008. "Theoretical measures of relative performance of classifiers for high dimensional data with small sample sizes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 159-173, February.
  42. Iván Blanco, Juan Ignacio Peña, and Rosa Rodriguez, 2018. "Modelling Electricity Swaps with Stochastic Forward Premium Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
  43. Rasmus Tangsgaard Varneskov, 2011. "Generalized Flat-Top Realized Kernel Estimation of Ex-Post Variation of Asset Prices Contaminated by Noise," CREATES Research Papers 2011-31, Department of Economics and Business Economics, Aarhus University.
  44. Jean-Marie DUFOUR & Lynda KHALAF & Marcel VOIA, 2013. "Finite-Sample Resampling-Based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability," Cahiers de recherche 13-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  45. Brière, Marie & Simar, Léopold & Szafarz, Ariane & Vanhems, Anne, 2023. "Sensitivity to measurement errors of the distance to the efficient frontier," LIDAM Discussion Papers ISBA 2023017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  46. Hidalgo, Javier, 2003. "An alternative bootstrap to moving blocks for time series regression models," Journal of Econometrics, Elsevier, vol. 117(2), pages 369-399, December.
  47. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
  48. Khurshid M. Kiani, 2009. "Asymmetries in Macroeconomic Time Series in Eleven Asian Economies," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 8(1), pages 37-54, April.
  49. Hoque, Hafiz A.A.B. & Kim, Jae H. & Pyun, Chong Soo, 2007. "A comparison of variance ratio tests of random walk: A case of Asian emerging stock markets," International Review of Economics & Finance, Elsevier, vol. 16(4), pages 488-502.
  50. Masato Ubukata & Kosuke Oya, 2008. "A Test for Dependence and Covariance Estimator of Market Microstructure Noise," Discussion Papers in Economics and Business 07-03-Rev.2, Osaka University, Graduate School of Economics.
  51. Horowitz, Joel L. & Lobato, I.N. & Nankervis, John C. & Savin, N.E., 2006. "Bootstrapping the Box-Pierce Q test: A robust test of uncorrelatedness," Journal of Econometrics, Elsevier, vol. 133(2), pages 841-862, August.
  52. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Journal of Econometrics, Elsevier, vol. 223(1), pages 125-160.
  53. Hilmer, Christiana E. & Holt, Matthew T., 2000. "A Comparison Of Resampling Techniques When Parameters Are On A Boundary: The Bootstrap, Subsample Bootstrap, And Subsample Jackknife," 2000 Annual meeting, July 30-August 2, Tampa, FL 21810, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  54. Kalnina, Ilze, 2011. "Subsampling high frequency data," Journal of Econometrics, Elsevier, vol. 161(2), pages 262-283, April.
  55. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.