This article questions current empirical practice in the study of growth. It argues that much of the modern empirical growth literature is based on assumptions about regressors, residuals, and parameters that are implausible from the perspective of both economic theory and the historical experiences of the countries under study. Many of these problems, it argues, are forms of violations of an exchangeability assumption that implicitly underlies standard growth exercises. The article shows that these implausible assumptions can be relaxed by allowing for uncertainty in model specification. Model uncertainty consists of two types: theory uncertainty, which relates to which growth determinants should be included in a model
and heterogeneity uncertainty, which relates to which observations in a data set constitute draw from the same statistical model. The article proposes ways to account for both theory and heterogeneity uncertainty. Finally, using an explicit decision-theoretic framework, the authors describe how one can engage in policy-relevant empirical analysis.