Arvin 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 -
