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Inference-Without-Smoothing in the Presence of Nonparametric Autocorrelation

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Author Info

  • P. M. Robinson

Abstract

The author gives conditions under which nonparametric autocorrelation-consistent variance estimation is possible without smoothing. The conditions are relevant to inference on slope parameters in models with an intercept and strictly exogenous regressors, and allow regressors and disturbances to collectively have considerable stationary long memory and to satisfy only mild, in some cases minimal, moment conditions. His estimate dominates smoothed ones in the sense that it can have mean squared error proportional to the reciprocal of sample size. Under standard additional regularity conditions, the author shows that the estimate can validly studentize asymptotically normal estimates of structural parameters in linear simultaneous equations systems.

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 66 (1998)
Issue (Month): 5 (September)
Pages: 1163-1182

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Handle: RePEc:ecm:emetrp:v:66:y:1998:i:5:p:1163-1182

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Cited by:
  1. Javier Hidalgo-Moreno & Myung Hwan Seo, 2012. "Testing for structural stability in the whole sample," Economics Working Papers we1236, Universidad Carlos III, Departamento de Economía.
  2. Horváth, Lajos & Husková, Marie & Kokoszka, Piotr, 2010. "Testing the stability of the functional autoregressive process," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 352-367, February.
  3. Tierney, Heather L.R., 2010. "Real-Time Data Revisions and the PCE Measure of Inflation," MPRA Paper 22387, University Library of Munich, Germany, revised Apr 2010.
  4. repec:cep:stiecm:em/2013/561 is not listed on IDEAS
  5. Javier Hidalgo, 2003. "An Alternative Bootstrap to Moving Blocks for Time Series Regression Models," STICERD - Econometrics Paper Series /2003/452, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  6. Marmol, Francesc & Velasco, Carlos, 2002. "Trend stationarity versus long-range dependence in time series analysis," Journal of Econometrics, Elsevier, vol. 108(1), pages 25-42, May.
  7. Wouter J. den Haan & Andrew T. Levin, 1995. "Inferences from parametric and non-parametric covariance matrix estimation procedures," International Finance Discussion Papers 504, Board of Governors of the Federal Reserve System (U.S.).
  8. Lazarova, Stepana, 2005. "Testing for structural change in regression with long memory processes," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 329-372.
  9. WANG, Kent & WANG, Shin-Huei & PAN, Zheyao, 2013. "Can federal reserve policy deviation explain response patterns of financial markets over time?," CORE Discussion Papers 2013029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. repec:cep:stiecm:em/2011/558 is not listed on IDEAS
  11. Tsay, Wen-Jen, 2004. "Testing for contemporaneous correlation of disturbances in seemingly unrelated regressions with serial dependence," Economics Letters, Elsevier, vol. 83(1), pages 69-76, April.
  12. 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.

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