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.
@article{bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1171,
author = {Arvin Paul B. Sumobay and Arnulfo P. Supe},
title = {Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme)},
journal = {Discussiones Mathematicae Probability and Statistics},
volume = {34},
year = {2014},
pages = {113-126},
zbl = {1326.62018},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1171}
}
Arvin Paul B. Sumobay; Arnulfo P. Supe. Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme). Discussiones Mathematicae Probability and Statistics, Tome 34 (2014) pp. 113-126. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1171/
[000] [1] G. Casella and R. Berger, Statistical Inference, First Edition (Brookes/Cole Publishing Company, 1990).
[001] [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).
[002] [3] L.M. Koyck, Distributed lags models and investment analysis (Amsterdam, North-Holland, 1954).
[003] [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).
[004] [5] A.P. Supe, Parameter changes in autoregressive processes: A Bayesian approach, Philippine Stat. J. 44-45 (1-8) (1996) 27-32.
[005] [6] B. Western and M. Kleykamp, A Bayesian Change Point Model for Historical Time Series Analysis (Princeton University, 2004).