IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v76y2006i4p349-352.html
   My bibliography  Save this article

On sampling stationary autoregressive model parameters uniformly in r2 value

Author

Listed:
  • Fitzgibbon, L.J.

Abstract

This paper describes a method of sampling stationary autoregressive models so that they are uniformly distributed in r2 value. A log-Gamma distribution, whose product density is uniformly distributed over [0,1], is used to sample partial autocorrelation parameters and obtain the desired result. This method can be used for the empirical evaluation of model selection and parameter estimation criteria.

Suggested Citation

  • Fitzgibbon, L.J., 2006. "On sampling stationary autoregressive model parameters uniformly in r2 value," Statistics & Probability Letters, Elsevier, vol. 76(4), pages 349-352, February.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:4:p:349-352
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(05)00305-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Barndorff-Nielsen, O. & Schou, G., 1973. "On the parametrization of autoregressive models by partial autocorrelations," Journal of Multivariate Analysis, Elsevier, vol. 3(4), pages 408-419, December.
    2. Vahid, Farshid & Issler, Joao Victor, 2002. "The importance of common cyclical features in VAR analysis: a Monte-Carlo study," Journal of Econometrics, Elsevier, vol. 109(2), pages 341-363, August.
    3. M. C. Jones, 1987. "Randomly Choosing Parameters from the Stationarity and Invertibility Region of Autoregressive–Moving Average Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 134-138, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Neuhoff, Daniel, 2015. "Dynamics of real per capita GDP," SFB 649 Discussion Papers 2015-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Meyer-Gohde, Alexander & Neuhoff, Daniel, 2015. "Generalized exogenous processes in DSGE: A Bayesian approach," SFB 649 Discussion Papers 2015-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Ng, Chi Tim & Joe, Harry, 2010. "Generating random AR(p) and MA(q) Toeplitz correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1532-1545, July.
    4. repec:hum:wpaper:sfb649dp2015-014 is not listed on IDEAS
    5. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    6. repec:hum:wpaper:sfb649dp2015-039 is not listed on IDEAS
    7. Pötscher, Benedikt M. & Preinerstorfer, David, 2018. "Controlling the size of autocorrelation robust tests," Journal of Econometrics, Elsevier, vol. 207(2), pages 406-431.
    8. Paroli, Roberta & Spezia, Luigi, 2008. "Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2311-2330, January.
    9. Paresh Kumar Narayan & Seema Narayan, 2008. "Do Permanent Shocks Explain Income Levels? A Common Cycle–Common Trend Analysis Of Regional Income Levels For China," Pacific Economic Review, Wiley Blackwell, vol. 13(5), pages 656-662, December.
    10. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    11. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    12. Sun, Wenbin & Price, Joseph & Ding, Yuan, 2019. "The longitudinal effects of internationalization on firm performance: The moderating role of marketing capability," Journal of Business Research, Elsevier, vol. 95(C), pages 326-337.
    13. Kapetanios, G. & Pagan, A. & Scott, A., 2007. "Making a match: Combining theory and evidence in policy-oriented macroeconomic modeling," Journal of Econometrics, Elsevier, vol. 136(2), pages 565-594, February.
    14. Marco Del Negro & Frank Schorfheide, 2009. "Monetary Policy Analysis with Potentially Misspecified Models," American Economic Review, American Economic Association, vol. 99(4), pages 1415-1450, September.
    15. Philippe, Anne, 2006. "Bayesian analysis of autoregressive moving average processes with unknown orders," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1904-1923, December.
    16. Tina Hviid Rydberg & Neil Shephard, 2003. "Dynamics of Trade-by-Trade Price Movements: Decomposition and Models," Journal of Financial Econometrics, Oxford University Press, vol. 1(1), pages 2-25.
    17. Athanasopoulos, George & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Journal of Econometrics, Elsevier, vol. 164(1), pages 116-129, September.
    18. Carlos Enrique Carrasco Gutiérrez & Reinaldo Castro Souza & Osmani Teixeira de Carvalho Guillén, 2007. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Working Papers Series 139, Central Bank of Brazil, Research Department.
    19. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
    20. Carsten Trenkler & Enzo Weber, 2013. "Codependent VAR models and the pseudo-structural form," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 287-295, July.
    21. Ilya Archakov & Peter Reinhard Hansen & Yiyao Luo, 2024. "A new method for generating random correlation matrices," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 188-212.
    22. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:76:y:2006:i:4:p:349-352. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.