Bayesian Model Averaging in Economic Growth Analysis

Bayesian Model Averaging (BMA) has emerged as a robust framework for addressing model uncertainty in empirical growth research. Rather than selecting a single ‘best’ specification, BMA combines the predictions of numerous plausible models, weighting each by its posterior probability derived from both prior beliefs and observed data. This approach mitigates the risk of overconfidence in any single model and yields more reliable estimates of the importance of potential growth determinants. In the context of cross-country growth regressions, BMA facilitates the systematic evaluation of a vast set of covariates—ranging from human capital and institutional quality to trade openness and geographical factors—while accounting for heterogeneity in parameter estimates and interdependence across economies. Recent methodological advances have extended BMA to accommodate panel‐data frameworks, time-varying coefficients and structural breaks, enhancing its capacity to capture dynamic growth processes. Applications span from long-term convergence analyses to crisis-related output losses, demonstrating the global significance of BMA for informing policy decisions and understanding the multifaceted drivers of economic expansion.

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Bayesian Model Averaging in Economic Growth Analysis publication trend

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Technical terms

Bayesian Model Averaging: A statistical technique that integrates outcomes over a range of candidate models, weighting each by its posterior probability based on prior beliefs and observed data.

Prior distribution: A specification of beliefs about parameters or models before analysing the current dataset, reflecting theoretical or empirical knowledge.

Posterior inclusion probability: The probability, conditional on the data, that a given variable should be included in the true underlying model.

Model uncertainty: The acknowledgement that multiple plausible model specifications exist, each capable of explaining the data to varying degrees, leading to uncertainty in variable selection.

References

1. The determinants of output losses during the Covid-19 pandemic. Economics Letters (2021).
2. Sources of Economic Growth: A Global Perspective. Sustainability (2019).
3. A Review of the ‘BMS’ Package for R with Focus on Jointness. Econometrics (2020).
4. An integrated panel data approach to modelling economic growth. Journal of Econometrics (2022).