IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

A linearly distributed lag estimator with r-convex coefficients

Listed author(s):
  • Vassiliou, E.E.
  • Demetriou, I.C.

The purpose of linearly distributed lag models is to estimate, from time series data, values of the dependent variable by incorporating prior information of the independent variable. A least-squares calculation is proposed for estimating the lag coefficients subject to the condition that the rth differences of the coefficients are non-negative, where r is a prescribed positive integer. Such priors do not assume any parameterization of the coefficients, and in several cases they provide such an accurate representation of the prior knowledge, so as to compare favorably to established methods. In particular, the choice of the prior knowledge parameter r gives the lag coefficients interesting special features such as monotonicity, convexity, convexity/concavity, etc. The proposed estimation problem is a strictly convex quadratic programming calculation, where each of the constraint functions depends on r+1 adjacent lag coefficients multiplied by the binomial numbers with alternating signs that arise in the expansion of the rth power of (1-1). The most distinctive feature of this calculation is the Toeplitz structure of the constraint coefficient matrix, which allows the development of a special active set method that is faster than general quadratic programming algorithms. Most of this efficiency is due to reducing the equality constrained minimization calculations, which occur during the quadratic programming iterations, to unconstrained minimization ones that depend on much fewer variables. Some examples with real and simulated data are presented in order to illustrate this approach.

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:
Download Restriction: Full text for ScienceDirect subscribers only.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2836-2849

in new window

Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2836-2849
Contact details of provider: Web page:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. Harezlak, Jaroslaw & Coull, Brent A. & Laird, Nan M. & Magari, Shannon R. & Christiani, David C., 2007. "Penalized solutions to functional regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4911-4925, June.
  2. Corradi, Corrado, 1977. "Smooth distributed lag estimators and smoothing spline functions in Hilbert spaces," Journal of Econometrics, Elsevier, vol. 5(2), pages 211-219, March.
  3. Polasek, Wolfgang, 1990. "Vector distributed lag models with smoothness priors," Computational Statistics & Data Analysis, Elsevier, vol. 10(2), pages 133-141, October.
  4. Shiller, Robert J, 1973. "A Distributed Lag Estimator Derived from Smoothness Priors," Econometrica, Econometric Society, vol. 41(4), pages 775-788, July.
Full references (including those not matched with items on IDEAS)

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

When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2836-2849. 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: (Dana Niculescu)

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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.