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A Bayesian Analysis of Autoregressive Time Series Panel Data

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  • Nandram, Balgobin
  • Petruccelli, Joseph D
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    Abstract

    The authors describe a Bayesian hierarchical model to analyze autoregressive time series panel data. They develop two algorithms using Markov-chain Monte Carlo methods, a restricted algorithm that enforces stationarity or nonstationarity conditions on the series, and an unrestricted algorithm that does not. Two examples show that restricting stationary series to be stationary provides no new information but restricting nonstationary series to be stationary leads to substantial differences from the unrestricted case. These examples and a simulation study also show that, compared with inference based on individual series, there are gains in precision for estimation and forecasting when similar series are pooled.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

    Volume (Year): 15 (1997)
    Issue (Month): 3 (July)
    Pages: 328-34

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    Handle: RePEc:bes:jnlbes:v:15:y:1997:i:3:p:328-34

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    Cited by:
    1. Choi, Hyunyoung & Finnerty, Joseph, 2006. "Impact study on the interest rate futures market," The Quarterly Review of Economics and Finance, Elsevier, vol. 46(4), pages 495-512, September.
    2. Cefis, Elena & Ciccarelli, Matteo & Orsenigo, Luigi, 2007. "Testing Gibrat's legacy: A Bayesian approach to study the growth of firms," Structural Change and Economic Dynamics, Elsevier, vol. 18(3), pages 348-369, September.
    3. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    4. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Non-Gaussian dynamic Bayesian modelling for panel data," MPRA Paper 450, University Library of Munich, Germany.
    5. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.

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