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Information about:
Michiel De Pooter

Personal Details | Affiliation | Works
This is information that was supplied by Michiel De Pooter in registering through RePEc. If you are Michiel De Pooter , you may change this information at RePEc. Or if you are not registered and would like to be listed as well, register at RePEc. When you register or update your RePEc registration, you may identify the papers and articles you have authored.

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Personal Details

First Name: Michiel
Middle Name:
Last Name: De Pooter
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RePEc Short-ID: pde371

Email: [This author has chosen not to make the email address public]
Homepage: http://people.few.eur.nl/depooter
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Affiliation

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Lists

This author is among the top 5% authors according to these criteria:
  1. Number of Downloads through RePEc Services over the past 12 months, Weighted by Number of Authors

Works

| Working papers | Articles | Access and download statistics | Citations (if any)| NEP Fields |
Download all references for this author: available formats: HTML (with abstracts), plain text (with abstracts), BibTeX, RIS (EndNote), ReDIF

Working papers

  1. Michiel De Pooter, 2007. "Examining the Nelson-Siegel Class of Term Structure Models," Tinbergen Institute Discussion Papers 07-043/4, Tinbergen Institute. [Downloadable!]

  2. De Pooter, Michiel & Ravazzolo, Francesco & van Dijk, Dick, 2006. "Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information," MPRA Paper 2512, University Library of Munich, Germany, revised 03 Mar 2007. [Downloadable!]
    Other versions:

  3. Michiel D. de Pooter & René Segers & Herman K. van Dijk, 2006. "On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling," Tinbergen Institute Discussion Papers 06-076/4, Tinbergen Institute. [Downloadable!]

  4. Michiel de Pooter & Martin Martens & Dick van Dijk, 2005. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data - But Which Frequency to Use?," Tinbergen Institute Discussion Papers 05-089/4, Tinbergen Institute, revised 03 Jan 2006. [Downloadable!]
    Published as:

  5. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute. [Downloadable!]

  6. Michiel D. de Pooter & Rengert Segers, 2004. "Learning the Shape of the Likelihood of Typical Econometric Models using Gibbs Sampling," Computing in Economics and Finance 2004 82, Society for Computational Economics.

Articles

  1. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303. [Downloadable!] (restricted)

  2. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data - But Which Frequency to Use?," Econometric Reviews, Taylor and Francis Journals, vol. 27(1-3), pages 199-229. [Downloadable!] (restricted)
    Other versions:

NEP Fields

6 papers by this author were announced in NEP, and specifically in the following field reports (number of papers):
  1. NEP-ECM: Econometrics (3) 2006-09-16 2007-03-17 2007-04-09 Author is listed
  2. NEP-ETS: Econometric Time Series (3) 2004-08-23 2005-11-19 2006-09-16 Author is listed
  3. NEP-FIN: Finance (2) 2004-08-23 2005-11-19 Author is listed
  4. NEP-FMK: Financial Markets (3) 2004-08-23 2005-11-19 2007-06-23 Author is listed
  5. NEP-FOR: Forecasting (3) 2007-03-17 2007-04-09 2007-06-23 Author is listed
  6. NEP-KNM: Knowledge Management & Knowledge Economy (1) 2006-09-16
  7. NEP-MAC: Macroeconomics (3) 2007-03-17 2007-04-09 2007-06-23 Author is listed
  8. NEP-MON: Monetary Economics (2) 2007-03-17 2007-04-09 Author is listed
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This page was last updated on 2009-11-27.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.