Growth Determinants Revisited Using Limited-Information Bayesian Model Averaging (replication data)

DOI

We revisit the growth empirics debate using a novel limited-information Bayesian model averaging framework in short T panels that addresses model uncertainty, dynamics, and endogeneity. We construct an estimator without restrictive distributional assumptions, illustrate its performance using simulations, and apply it to the investigation of growth determinants. Once model uncertainty, dynamics, and endogeneity are accounted for, we identify several factors that are robustly correlated with growth. We find the strongest support for the neoclassical growth variables including initial income and proxies for physical and human capital accumulation, as well as evidence in favor of both fundamental and proximate factors including macroeconomic policy, geography, and ethnic heterogeneity. In addition, we demonstrate that applying methodologies that do not account for either dynamics or endogeneity yields different sets of robust determinants.

Identifier
DOI https://doi.org/10.15456/jae.2022326.0656132586
Metadata Access https://www.da-ra.de/oaip/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:oai.da-ra.de:775561
Provenance
Creator Mirestean, Alin; Tsangarides, Charalambos G.
Publisher ZBW - Leibniz Informationszentrum Wirtschaft
Publication Year 2016
Rights Creative Commons Attribution 4.0 (CC-BY); Download
OpenAccess true
Contact ZBW - Leibniz Informationszentrum Wirtschaft
Representation
Language English
Resource Type Collection
Discipline Economics; Social and Behavioural Sciences