In this paper I estimate empirical growth models simultaneaously considering endogenous regressors and model uncertainty. In order to apply Bayesian methods such as Bayesian Model Averaging (BMA) to dynamic panel data models with predetermined or endogenous variables and fixed effects, I propose...

Fragility of regression analysis to arbitrary assumptions and decisions about choice of control variables is an important concern for applied econometricians (e.g. Leamer (1983)). Sensitivity analysis in the form of model averaging represents an (agnostic) approach that formally addresses this...

For reason of empirical tractability, analysis of cointegrated economic time series is often developed in a partial setting, in which a subset of variables is explicitly modeled conditional on the rest. This approach yields valid inference only if the conditioning variables are weakly exogenous...

"Model uncertainty hampers consensus on the key determinants of economic growth. Some recent cross-country, cross-sectional analyses have employed Bayesian Model Averaging to address the issue of model uncertainty. This paper extends that approach to panel data models with country-specific fixed...

"Model uncertainty hampers consensus on the key determinants of economic growth. Some recent cross-country, cross-sectional analyses have employed Bayesian Model Averaging to address the issue of model uncertainty. This paper extends that approach to panel data models with country-specific fixed...