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2014 | 34 | 1-2 | 113-126
Tytuł artykułu

Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme)

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EN
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EN
Structural change for the Koyck Distributed Lag Model is analyzed through the Bayesian approach. The posterior distribution of the break point is derived with the use of the normal-gamma prior density and the break point, ν, is estimated by the value that attains the Highest Posterior Probability (HPP). Simulation study is done using R.
Given the parameter values ϕ = 0.2 and λ = 0.3, the full detection of the structural change when σ² = 1 is generally attained at ν + 1. The after one lag detection is due to the nature of the model which includes lagged variable. The interval estimate HPP near ν consistently and efficiently captures the break point ν in the interval HPPₜ ± 5% of the sample size. On the other hand, the detection of the structural change when σ² = 2 does not show any improvement of the point estimate of the break point ν.
Twórcy
  • Mathematics Department, Mindanao State University, Marawi City, 9700, Philippines
  • Department of Mathematics and Statistics, Mindanao State University-Iligan Institute of Technology, Iligan City, 9200, Philippines
Bibliografia
  • [1] G. Casella and R. Berger, Statistical Inference, First Edition (Brookes/Cole Publishing Company, 1990).
  • [2] A. Chaturvedia and A. Shrivastavab, Bayesian Analysis of a Linear Model Involving Structural Changes in Either Regression Parameters or Disturbances Precision (Department of Statistics, University of Allahabad, Allahabad U.P 211002 India, 2012).
  • [3] L.M. Koyck, Distributed lags models and investment analysis (Amsterdam, North-Holland, 1954).
  • [4] J.H. Park, Bayesian Analysis of Structural Changes: Historical Changes in US Presidential Uses of Force (Annual Meeting of the Society for Political Methodology, 2007).
  • [5] A.P. Supe, Parameter changes in autoregressive processes: A Bayesian approach, Philippine Stat. J. 44-45 (1-8) (1996) 27-32.
  • [6] B. Western and M. Kleykamp, A Bayesian Change Point Model for Historical Time Series Analysis (Princeton University, 2004).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1171
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