TFP in Germany and the USA (2000–2017): Classical and Bayesian Approaches to Trend Analysis
TFP in Germany and the USA (2000–2017): Classical and Bayesian Approaches to Trend Analysis

Summary/Abstract: The study aims to analyse the trends in total factor productivity (TFP) in Germany and the USA between 2000 and 2017, assessing whether productivity growth differs significantly between the two economies. TFP was calculated using the Cobb–Douglas production function and the Solow residual. Trends were estimated through linear and interaction models, while robustness was assessed with nonparametric permutation tests. In addition, a Bayesian panel model was employed to incorporate prior knowledge about the production function and to obtain more stable estimates. Posterior tests were applied to evaluate model fit and the reliability of results. Parametric estimates indicated statistically significantly higher TFP growth in the USA compared to Germany. However, nonparametric permutation tests did not confirm these differences, underlining the limitations of short time series. The Bayesian panel approach provided consistent, robust results, supporting the validity of combining classical and Bayesian techniques. Despite the short time span and limited sample, the study demonstrates a novel methodological framework for comparative productivity analysis. By combining frequentist and Bayesian approaches, it highlights the potential for more robust inference. It provides a model that can be applied to analyses of other countries or extended time horizons.

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