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Markov chain approximation in bootstrapping autoregressions

Author

Listed:
  • Stanislav Anatolyev

    (New Economic School, Moscow)

  • Andrey Vasnev

    (New Economic School, Moscow)

Abstract

We propose a bootstrap algorithm for autoregressions based on the approximation of the data generating process by a finite state discrete Markov chain. We discover a close connection of the proposed algorithm with existing bootstrap resampling schemes, run a small Monte-Carlo experiment, and give an illustrative example.

Suggested Citation

  • Stanislav Anatolyev & Andrey Vasnev, 2002. "Markov chain approximation in bootstrapping autoregressions," Economics Bulletin, AccessEcon, vol. 3(19), pages 1-8.
    • Handle: RePEc:ebl:ecbull:eb-02c10004
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    References listed on IDEAS

    as
    1. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
    2. M. Rajarshi, 1990. "Bootstrap in Markov-sequences based on estimates of transition density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(2), pages 253-268, June.
    3. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    4. Gregory, Allan W, 1989. "A Nonparametric Test for Autoregressive Conditional Heteroscedasticity: A Markov-Chain Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 107-115, January.
    5. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
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    Citations

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    Cited by:

    1. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2012. "A Mixed Integer Linear Programming Approach to Markov Chain Bootstrapping," Working Papers 67-2012, Macerata University, Department of Finance and Economic Sciences, revised Nov 2012.
    2. Cerqueti, Roy & Falbo, Paolo & Guastaroba, Gianfranco & Pelizzari, Cristian, 2013. "A Tabu Search heuristic procedure in Markov chain bootstrapping," European Journal of Operational Research, Elsevier, vol. 227(2), pages 367-384.
    3. Cerqueti, Roy & Falbo, Paolo & Pelizzari, Cristian, 2017. "Relevant states and memory in Markov chain bootstrapping and simulation," European Journal of Operational Research, Elsevier, vol. 256(1), pages 163-177.
    4. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2017. "A mixed integer linear program to compress transition probability matrices in Markov chain bootstrapping," Annals of Operations Research, Springer, vol. 248(1), pages 163-187, January.

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    More about this item

    Keywords

    Bootstrap resampling in time series;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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