Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-23T06:33:18.306Z Has data issue: false hasContentIssue false
  • English
  • Français

HOW IMPORTANT IS INNOVATION? A BAYESIAN FACTOR-AUGMENTED PRODUCTIVITY MODEL BASED ON PANEL DATA

Published online by Cambridge University Press:  01 August 2016

Georges Bresson ,
Jean-Michel Etienne  and
Pierre Mohnen
Georges Bresson
Affiliation:
Université Paris II and Sorbonne Universités
Jean-Michel Etienne
Affiliation:
Université Paris-Sud 11
Pierre Mohnen*
Affiliation:
UNU-MERIT, Maastricht University
*
Address correspondence to: Pierre Mohnen, UNU-MERIT Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; e-mail : [email protected].
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Keywords

Bayesian Factor-Augmented ModelInnovationMarkov Chain Monte CarloPanel DataProductivity
Type
Articles
Information
Macroeconomic Dynamics , Volume 20 , Special Issue 8: Technology Aspects in the Process of Development , December 2016 , pp. 1987 - 2009
Copyright
Copyright © Cambridge University Press 2016 

References

REFERENCES

Abramowitz, Moses (1956) Resource and output trends in the United States since 1870. American Economic Review 46 (2), 5–23.Google Scholar
Allen, Stuart J. and Hubbard, Raymond (1986) Regression equations of the latent roots of random data correlation matrices unities on the diagonal. Multivariate Behavioral Research 21, 393–398.Google Scholar
Anderson, Heather M., Issler, João Victor, and Valid, Farshid (2006) Common features. Journal of Econometrics 132, 1–15.Google Scholar
Bai, Jushan and Ng, Serena (2002) Determining the number of factors in approximate factor models. Econometrica 70, 191–221.Google Scholar
Bai, Jushan and Ng, Serena (2006) Confidence intervals for diffusion index forecasts and inference with factor-augmented regressions. Econometrica 74, 1133–1150.CrossRefGoogle Scholar
Bai, Jushan and Ng, Serena (2008) Large dimensional factor analysis. Foundations and Trends in Econometrics 3 (2), 89–163.CrossRefGoogle Scholar
Bartholomew, David J., Deary, Ian J., and Lawn, Martin (2009) The origin of factor scores: Spearman, Thomson and Bartlett. British Journal of Mathematical and Statistical Psychology 62, 569–582.Google Scholar
Bresson, Georges and Hsiao, Cheng (2011) A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris. Advances in Statistical Analysis 95 (4), 501–529.Google Scholar
Bresson, Georges, Hsiao, Cheng, and Pirotte, Alain (2011) Assessing the contribution of R&D to total factor productivity—A Bayesian approach to account for heterogeneity and heteroscedasticity. Advances in Statistical Analysis 95 (4), 435–452.Google Scholar
Brooks, Stephen P. and Gelman, Andrew (1998) Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434–455.Google Scholar
Castellacci, Fulvio and Natera, Jose Miguel (2011) A New Panel Dataset for Cross-Country Analyses of National Systems, Growth and Development (CANA). Munich personal RePEc archive paper 28376.CrossRefGoogle Scholar
Chamberlain, Gary (1980) Analysis of covariance with qualitative data. Review of Economic Studies 47, 225–238.Google Scholar
Cornell University, INSEAD, WIPO (2014) The Global Innovation Index 2014: The Human Factor in Innovation. Ithaca, NY.Google Scholar
Durlauf, Steven N., Johnson, Paul A., and Temple, Jonathan R. (2005) Growth econometrics. In Aghion, Philippe and Durlauf, Steven (eds.), Handbook of Economic Growth, Vol. 1, pp. 555–677. Amsterdam: Elsevier.Google Scholar
Easterly, William and Levine, Ross (2001) What have we learned from a decade of empirical research on growth? It's not factor accumulation: Stylized facts and growth models. World Bank Economic Review 15 (2), 177–219.Google Scholar
Eberhardt, Markus and Bond, Steve (2009) Cross-Section Dependence in Nonstationary Panel Models: A Novel Estimator. University Library of Munich, Munich personal RePEc archive paper 01/2009.Google Scholar
Eberhardt, Markus and Teal, Francis (2011) Econometrics for grumblers: A new look at the literature on cross-country growth empirics. Journal of Economic Surveys 25 (1), 109–155.Google Scholar
Fagerberg, Jan and Srholec, Martin (2008) National innovation systems, capabilities and economic development. Research Policy 37, 1417–1435.Google Scholar
Fernandez, Carmen, Ley, Edurado, and Steel, Mark (2001) Model uncertainty in cross-country growth regressions. Journal of Applied Econometrics 16, 563–576.Google Scholar
Gonçcalves, Silvia and Perron, Benoît (2012) Bootstrapping Factor-Augmented Regression Models. CIRANO working paper 2012s-12.Google Scholar
Gospodinov, Nikolay and Ng, Serena (2013) Commodity prices, convenience yields and inflation. Review of Economics and Statistics 95 (1), 206–219.Google Scholar
Hecq, Alain, Palm, Franz, and Urbain, Jean-Pierre (2006) Common cyclical features analysis in VAR models with cointegration. Journal of Econometrics 132 (1), 117–141.Google Scholar
Holtz-Eakin, Douglas, Newey, Whitney, and Rosen, Harvey (1988) Estimating vector autoregressions with panel data. Econometrica 56, 1371–1395.Google Scholar
Kneip, Alois, Sickles, Robin C., and Song, Wonho (2012) A new panel data treatment for heterogeneity in time trends. Econometric Theory 28, 590–628.CrossRefGoogle Scholar
Komunjer, Ivana and Ng, Serena (2010) Indirect Estimation of Models with Latent Error Components. Mimeo, Columbia University.Google Scholar
Lanjouw, Jean O. and Schankermann, Mark (2004) Patent quality and research productivity: Measuring innovations with multiple indicators. Economic Journal 114, 441–465.Google Scholar
Lindley, Dennis V. and Smith, Adrian F.M. (1972) Bayes estimates for the linear model. Journal of the Royal Statistical Society B 34, 1–41.Google Scholar
Ludvigson, Sydney and Ng, Serena (2009) Macro factors in bond risk premia. Review of Financial Studies 22, 5027–5067.CrossRefGoogle Scholar
Moench, Emanuel, Ng, Serena, and Potter, Simon (2009) Dynamic Hierarchical Factor Models. Federal Reserve Bank of New York staff report 412, December.Google Scholar
Moral-Benito, Enrique (2012) Determinants of economic growth: A Bayesian panel data approach. Review of Economics and Statistics 94 (2), 566–579.CrossRefGoogle Scholar
Nickell, Stephen (1981) Biases in dynamic models with fixed effects. Econometrica 49, 1417–1426.Google Scholar
Pesaran, M. Hashem (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74 (4), 967–1012.Google Scholar
Press, S. James and Shigemasu, Kazuo (1997) Bayesian Inference in Factor Analysis. Technical report 243 (revised), Department of Statistics, University of California, Riverside.Google Scholar
Roberts, Gareth O. (1995) Markov chain concepts related to sampling algorithms. In Gilks, Wally R., Spiegelhalter, David, and Richardson, Sylvia (eds.), MCMC in Practice, pp. 45–58. London: Chapman and Hall.Google Scholar
Solow, Robert (1957) Technical change and the aggregate production function. Review of Economics and Statistics 39, 312–320.Google Scholar
Spiegelhalter, David, Thomas, Andrew, and Best, Nicky (2000) WinBUGS, Bayesian Inference Using Gibbs Sampling, Version 1.3. User manual, MRC Biostatistics Unit, Cambridge, UK.Google Scholar
Stock, James H. and Watson, Mark (2002) Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97, 1167–1179.Google Scholar
Supplementary material: PDF

Bresson supplementary material

Figures S1-S5 and Tables S1-S3

Download Bresson supplementary material(PDF)
PDF 646 KB