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

Arvin Paul B. Sumobay; Arnulfo P. Supe

Discussiones Mathematicae Probability and Statistics (2014)

- Volume: 34, Issue: 1-2, page 113-126
- ISSN: 1509-9423

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topArvin Paul B. Sumobay, and Arnulfo P. Supe. "Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme)." Discussiones Mathematicae Probability and Statistics 34.1-2 (2014): 113-126. <http://eudml.org/doc/271044>.

@article{ArvinPaulB2014,

abstract = {
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.
},

author = {Arvin Paul B. Sumobay, Arnulfo P. Supe},

journal = {Discussiones Mathematicae Probability and Statistics},

keywords = {distributed lag model; posterior distribution; break point},

language = {eng},

number = {1-2},

pages = {113-126},

title = {Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme)},

url = {http://eudml.org/doc/271044},

volume = {34},

year = {2014},

}

TY - JOUR

AU - Arvin Paul B. Sumobay

AU - Arnulfo P. Supe

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

JO - Discussiones Mathematicae Probability and Statistics

PY - 2014

VL - 34

IS - 1-2

SP - 113

EP - 126

AB -
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.

LA - eng

KW - distributed lag model; posterior distribution; break point

UR - http://eudml.org/doc/271044

ER -

## References

top- [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).

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