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

Block Bootstrap for Parameter Estimation Error when Parameters are recursively estimated

Contents:

Author Info

  • Norman R. Swanson
  • Valentina Corradi

Abstract

In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations are used and sequence of P estimators are constructed. This paper provides a new block bootstrap procedure that mimics the limiting distribution of the scaled sum of the difference between the P estimators and their probability limit. We consider the case of m-estimators. In the recursive case, earlier observations are used more frequently than temporally subsequent observations. This introduces a bias to the usual block bootstrap, as any block from the original sample has the same probability of being selected. We circumvent this problem by first forming blocks as follows. Resample R observations from the first R observations, and then concatenate onto this vector an additional P resampled observations from the remaining sample. Construct a sequence of P bootstrap estimators, using the resampled series. Thereafter, construct the sum (scaled by sqrt P) of the difference between the P bootstrap estimators and the P actual estimators, and add an adjustment term in order to ensure that the sum of the two has the same limiting distribution as the sum (scaled by sqrtP) of the difference between the P (actual) estimators and their probability limits. This recursive block bootstrap can be used to provide valid critical values in a variety of interesting testing contexts, and three such leading applications are developed. The first is a generalization of the reality check test of White (2000) for the case of non-vanishing parameter estimation error. The second is an out of sample version of the integrated conditional moment test of Bierens (1982, 1990) and Bierens and Ploberger (1997) which provides out of sample tests consistent against generic (nonlinear) alternatives. Finally, the third is a procedure for assessing the relative out of sample accuracy of multiple conditional distribution models. This procedure can be viewed as an extension of the Andrews (1997) conditional Kolmogorov test.

Download Info

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 264.

as in new window
Length:
Date of creation: 11 Aug 2004
Date of revision:
Handle: RePEc:ecm:nawm04:264

Contact details of provider:
Phone: 1 212 998 3820
Fax: 1 212 995 4487
Email:
Web page: http://www.econometricsociety.org/pastmeetings.asp
More information through EDIRC

Related research

Keywords: block bootstrap; conditional distributions; misspecification; parameter estimation error;

Find related papers by JEL classification:

References

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

Citations

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:ecm:nawm04:264. 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: (Christopher F. Baum).

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.