TY - JOUR
T1 - How Important is Innovation?
T2 - A Bayesian Factor-Augmented Productivity Model Based on Panel Data
AU - Bresson, G.
AU - Etienne, J.-M.
AU - Mohnen, P.
N1 - data source: World Bank, World Development Indicators, CANA dataset (Castellaci and Natera, 2011, REPEC Archive Data 28376)
Export Date: 8 December 2016
Correspondence Address: Mohnen, P.; UNU-MERIT, Maastricht University, P.O. Box 616, Netherlands; email: [email protected]
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PY - 2016/12
Y1 - 2016/12
N2 - This paper proposes a Bayesian approach to estimating a factor-augmented GDP per capita equation. We exploit the panel dimension of our data and distinguish between individual-specific and time-specific factors. On the basis of 21 technology, infrastructure, and institutional indicators from 82 countries over a 19-year period (1990 to 2008), we construct summary indicators of each of these three components in the cross-sectional dimension and an overall indicator of all 21 indicators in the time-series dimension and estimate their effects on growth and international differences in GDP per capita. For most countries, more than 50% of GDP per capita is explained by the four common factors we have introduced. Infrastructure is the greatest contributor to total factor productivity, followed by technology and institutions. © Cambridge University Press 2016.
AB - This paper proposes a Bayesian approach to estimating a factor-augmented GDP per capita equation. We exploit the panel dimension of our data and distinguish between individual-specific and time-specific factors. On the basis of 21 technology, infrastructure, and institutional indicators from 82 countries over a 19-year period (1990 to 2008), we construct summary indicators of each of these three components in the cross-sectional dimension and an overall indicator of all 21 indicators in the time-series dimension and estimate their effects on growth and international differences in GDP per capita. For most countries, more than 50% of GDP per capita is explained by the four common factors we have introduced. Infrastructure is the greatest contributor to total factor productivity, followed by technology and institutions. © Cambridge University Press 2016.
KW - Bayesian Factor-Augmented Model
KW - Innovation
KW - Markov Chain Monte Carlo
KW - Panel Data
KW - Productivity
KW - HETEROGENEITY
KW - NUMBER
KW - PRICES
KW - INFERENCE
KW - TRENDS
KW - REGRESSIONS
KW - FEATURES
U2 - 10.1017/S1365100515000371
DO - 10.1017/S1365100515000371
M3 - Article
SN - 1365-1005
VL - 20
SP - 1987
EP - 2009
JO - Macroeconomic Dynamics
JF - Macroeconomic Dynamics
IS - 8
ER -