## Abstract

Original language | English |
---|---|

Pages (from-to) | 1987-2009 |

Number of pages | 23 |

Journal | Macroeconomic Dynamics |

Volume | 20 |

Issue number | 8 |

DOIs | |

Publication status | Published - Dec 2016 |

## Keywords

- Bayesian Factor-Augmented Model
- Innovation
- Markov Chain Monte Carlo
- Panel Data
- Productivity
- HETEROGENEITY
- NUMBER
- PRICES
- INFERENCE
- TRENDS
- REGRESSIONS
- FEATURES

## Cite this

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*Macroeconomic Dynamics*,

*20*(8), 1987-2009. https://doi.org/10.1017/S1365100515000371

**How Important is Innovation? A Bayesian Factor-Augmented Productivity Model Based on Panel Data**. In: Macroeconomic Dynamics. 2016 ; Vol. 20, No. 8. pp. 1987-2009.

}

*Macroeconomic Dynamics*, vol. 20, no. 8, pp. 1987-2009. https://doi.org/10.1017/S1365100515000371

**How Important is Innovation? A Bayesian Factor-Augmented Productivity Model Based on Panel Data.**/ Bresson, G.; Etienne, J.-M.; Mohnen, P.

In: Macroeconomic Dynamics, Vol. 20, No. 8, 12.2016, p. 1987-2009.

Research output: Contribution to journal › Article › Academic › peer-review

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

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 -