An Analysis of the Impact of Research and Development on Productivity Using Bayesian Model Averaging with a Reversible Jump Algorithm

A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of (R&D) on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverse...

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Published inAmerican journal of agricultural economics Vol. 92; no. 4; pp. 985 - 998
Main Authors Balcombe, Kelvin, Rapsomanikis, George
Format Journal Article
LanguageEnglish
Published Malden Oxford University Press 01.07.2010
Oxford University press
Blackwell Publishing Ltd
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Online AccessGet full text
ISSN0002-9092
1467-8276
1467-8276
DOI10.1093/ajae/aaq050

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Abstract A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of (R&D) on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverses the model space. In addition to producing estimates and standard deviations for the coefficients, the probability that a given lag (or break) enters the model is estimated. The approach is extended to select models populated with gamma distributed lags of different frequencies. Results are consistent with the hypothesis that R&D positively drives productivity. Gamma lags are found to retain their usefulness in imposing a plausible structure on lag coefficients, and their role is enhanced through the use of model averaging.
AbstractList A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of R&D on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverses the model space. In addition to producing estimates and standard deviations for the coefficients, the probability that a given lag (or break) enters the model is estimated. The approach is extended to select models populated with gamma distributed lags of different frequencies. Results are consistent with the hypothesis that R&D positively drives productivity. Gamma lags are found to retain their usefulness in imposing a plausible structure on lag coefficients, and their role is enhanced through the use of model averaging. Reprinted by permission of the American Agricultural Economics Association
A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of R&D on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverses the model space. In addition to producing estimates and standard deviations for the coefficients, the probability that a given lag (or break) enters the model is estimated. The approach is extended to select models populated with gamma distributed lags of different frequencies. Results are consistent with the hypothesis that R&D positively drives productivity. Gamma lags are found to retain their usefulness in imposing a plausible structure on lag coefficients, and their role is enhanced through the use of model averaging.
A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of (R&D) on agricultural productivity in the United States from 1889 to 1990. Lag and structural break coefficients are estimated using a reversible jump algorithm that traverses the model space. In addition to producing estimates and standard deviations for the coefficients, the probability that a given lag (or break) enters the model is estimated. The approach is extended to select models populated with gamma distributed lags of different frequencies. Results are consistent with the hypothesis that R&D positively drives productivity. Gamma lags are found to retain their usefulness in imposing a plausible structure on lag coefficients, and their role is enhanced through the use of model averaging. [PUBLICATION ABSTRACT]
Author Rapsomanikis, George
Balcombe, Kelvin
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2010 Agricultural and Applied Economics Association
Copyright Oxford University Press, UK Jul 2010
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Issue 4
Keywords agriculture
C22
reversible jump
productivity
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Bayesian
research and development
C51
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The views expressed in this article are those of the authors and do not necessarily reflect the views of the Food and Agriculture Organization of the United Nations.
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Snippet A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of (R&D) on agricultural productivity in...
A Bayesian model averaging approach to the estimation of lag structures is introduced and applied to assess the impact of R&D on agricultural productivity in...
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StartPage 985
SubjectTerms 1889-1990
Agrarwissenschaft
Agricultural development
Agricultural economics
Agricultural production
Agricultural productivity
agriculture
Agronomy
Algorithms
Bayesian
Bayesian analysis
Bayesian method
Bayesian networks
Bayesian theory
Breaking
C22
C51
Economic models
Economics
Empirical evidence
Gestation period
Impact analysis
Landwirtschaft
Mathematical models
Parametric models
probability
Productivity
Produktivität
R&D
Research & development
Research and development
Reversible
reversible jump
Standard deviation
Studies
United States
USA
Usefulness
Title An Analysis of the Impact of Research and Development on Productivity Using Bayesian Model Averaging with a Reversible Jump Algorithm
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https://www.proquest.com/docview/1082210595
https://www.proquest.com/docview/2431857908
https://www.proquest.com/docview/815278745
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1093/ajae/aaq050
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