Add to ORKGFrance, for hospitality during the preparation of this paper. The views expressed in this study are the sole responsibility of the authors and should not be attributed to the International Monetary Fund, its Executive Board, or its management. Economic growth has been a showcase of model uncertainty, given the many competing theories and candidate regressors that have been proposed to explain growth. Bayesian Model Averaging (BMA) addresses model uncertainty as part of the empirical strategy, but its implementation is subject to the choice of priors: the priors for the parameters in each model, and the prior over the model space. For a well-known growth dataset, we show that model choice can be sensitive to th
The issue of model uncertainty is central to the empirical study of economic growth. Many recent pap...
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Mod...
Ciccone and Jarociński (American Economic Journal: Macroeconomics 2010; 2: 222-246) show that infere...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical grow...
Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. T...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
Quantitative growth economists often have to deal with model uncertainty (Barro et al. (2003)) and t...
The method of model averaging has become an important tool to deal with model uncertainty, for exam...
CHAPTER 1:The default g-priors predominant in Bayesian Model Averaging tend to over-concentrate post...
Entrepreneurship has long been seen as an important instrument in stimulating and generating economi...
The question whether financial development is conducive to economic growth has entered the debate wi...
The issue of model uncertainty is central to the empirical study of economic growth. Many recent pap...
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Mod...
Ciccone and Jarociński (American Economic Journal: Macroeconomics 2010; 2: 222-246) show that infere...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical grow...
Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. T...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
Quantitative growth economists often have to deal with model uncertainty (Barro et al. (2003)) and t...
The method of model averaging has become an important tool to deal with model uncertainty, for exam...
CHAPTER 1:The default g-priors predominant in Bayesian Model Averaging tend to over-concentrate post...
Entrepreneurship has long been seen as an important instrument in stimulating and generating economi...
The question whether financial development is conducive to economic growth has entered the debate wi...
The issue of model uncertainty is central to the empirical study of economic growth. Many recent pap...
We consider the problem of variable selection in linear regression models. Bayesian model averaging ...
The method of model averaging has become an important tool to deal with model uncertainty, for examp...