Advanced Search
MyIDEAS: Login to save this paper or follow this series

Consistent HAC Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation

Contents:

Author Info

  • Peter C.B. Phillips

    ()
    (Yale University, Cowles Foundation)

  • Sainan Jin

    ()
    (Yale University, Faculty of Arts & Sciences, Department of Economics (Box 8268))

  • Yixiao Sun

    ()
    (University of California, San Diego, Division of Social Sciences, Department of Economics)

Abstract

A new family of kernels is suggested for use in heteroskedasticity and autocorrelation consistent (HAC) and long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or bandwidth) parameter. As the power parameter (rho) increases, the kernels become very sharp at the origin and increasingly downweight values away from the origin, thereby achieving effects similar to a bandwidth parameter. Sharp origin kernels can be used in regression testing in much the same way as conventional kernels with no truncation, as suggested in the work of Kiefer and Vogelsang (2002a, 2002b). A unified representation of HAC limit theory for untruncated kernels is provided using a new proof based on Mercer's theorem that allows for kernels which may or may not be differentiable at the origin. This new representation helps to explain earlier findings like the dominance of the Bartlett kernel over quadratic kernels in test power and yields new findings about the asymptotic properties of tests with sharp origin kernels. Analysis and simulations indicate that sharp origin kernels lead to tests with improved size properties relative to conventional tests and better power properties than other tests using Bartlett and other conventional kernels without truncation. If rho is passed to infinity with the sample size (T), the new kernels provide consistent HAC and LRV estimates as well as continued robust regression testing. Optimal choice of rho based on minimizing the asymptotic mean squared error of estimation is considered, leading to a rate of convergence of the kernel estimate of T1/3, analogous to that of a conventional truncated Bartlett kernel estimate with an optimal choice of bandwidth. A data-based version of the consistent sharp origin kernel is obtained which is easily implementable in practical work. Within this new framework, untruncated kernel estimation can be regarded as a form of conventional kernel estimation in which the usual bandwidth parameter is replaced by a power parameter that serves to control the degree of downweighting. Simulations show that in regression testing with the sharp origin kernel, the power properties are better than those with simple untruncated kernels (where rho = 1) and at least as good as those with truncated kernels. Size is generally more accurate with sharp origin kernels than truncated kernels. In practice a simple fixed choice of the exponent parameter around rho = 16 for the sharp origin kernel produces favorable results for both size and power in regression testing with sample sizes that are typical in econometric applications.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=386084
Download Restriction: no

Bibliographic Info

Paper provided by Yale School of Management in its series Yale School of Management Working Papers with number ysm347.

as in new window
Length:
Date of creation: 28 Jul 2004
Date of revision:
Handle: RePEc:ysm:somwrk:ysm347

Contact details of provider:
Web page: http://icf.som.yale.edu/
More information through EDIRC

Related research

Keywords: Consistent HAC Estimation; Data Determined Kernel Estimation; Long Run Variance; Mercer's Theorem; Power Parameter; Sharp Origin Kernel;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

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

Cited by:
  1. João Valle e Azevedo, 2011. "Rational vs. professional forecasts," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  2. Richard Smith, 2004. "Automatic positive semi-definite HAC covariance matrix and GMM estimation," CeMMAP working papers CWP17/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Peter C.B. Phillips & Yixiao Sun & Sainan Jin, 2005. "Improved HAR Inference," Cowles Foundation Discussion Papers 1513, Cowles Foundation for Research in Economics, Yale University.
  4. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
  5. Bernard Fingleton & Michelle Catherine Baddeley, 2011. "Globalisation And Wage Differentials: A Spatial Analysis," Manchester School, University of Manchester, vol. 79(5), pages 1018-1034, 09.
  6. Surajit Ray & N. E. Savin, 2008. "The performance of heteroskedasticity and autocorrelation robust tests: a Monte Carlo study with an application to the three-factor Fama-French asset-pricing model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 91-109.
  7. Jen-Je Su, 2005. "On the size and power of testing for no autocorrelation under weak assumptions," Applied Financial Economics, Taylor & Francis Journals, vol. 15(4), pages 247-257.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ysm:somwrk:ysm347. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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