This paper starts with a brief description of the introduction of the likelihood approach in econometrics as presented in Cowles Foundation Monographs 10 and 14. A sketch is given of the criticisms on this approach mainly from the first group of Bayesian econometricians. Publication and citation patterns of Bayesian econometric papers are analyzed in ten major econometric journals from the late 1970s until the first few months of 2014. Results indicate a cluster of journals with theoretical and applied papers, mainly consisting of Journal of Econometrics, Journal of Business and Economic Statistics and Journal of Applied Econometrics which contains the large majority of high quality Bayesian econometric papers. A second cluster of theoretical journals, mainly consisting of Econometrica and Review of Economic Studies contains few Bayesian econometric papers. The scientific impact, however, of these few papers on Bayesian econometric research is substantial. Special issues from the journals Econometric Reviews, Journal of Econometrics and Econometric Theory received wide attention. Marketing Science shows an ever increasing number of Bayesian papers since the middle nineties. The International Economic Review and the Review of Economics and Statistics show a moderate time varying increase. An upward movement in publication patterns in most journals occurs in the early 1990s due to the effect of the ‘Computational Revolution’. The paper continues using a visualization technique to connect papers and authors around important empirical subjects such as forecasting in macro models and finance, choice and equilibrium in micro models and marketing, and around more methodological subjects as model uncertainty and sampling algorithms. The information distilled from this analysis shows names of authors who contribute substantially to particular subjects. Next, subjects are discussed where Bayesian econometrics has shown substantial advances, namely, implementing stochastic simulation methods due to the computational revolution; flexible and unobserved component model structures in macroeconomics and finance; hierarchical structures and choice models in microeconomics and marketing. Three issues are summarized where Bayesian and frequentist econometricians differ: Identification, the value of prior information and model evaluation; dynamic inference and nonstationarity; vector autoregressive versus structural modeling. A topic of debate amongst Bayesian econometricians is listed as objective versus subjective econometrics. Communication problems and bridges between statistics and econometrics are summarized. A few non-Bayesian econometric papers are listed that have had substantial influence on Bayesian econometrics. Recent advances in applying simulation based Bayesian econometric methods to policy issues using models from macro- and microeconomics, finance and marketing are sketched. The paper ends with a list of subjects that are important challenges for twenty-first century Bayesian econometrics: Sampling methods suitable for use with big data and fast, parallelized and GPU calculations, complex economic models which account for nonlinearities, analysis of implied model features such as risk and instability, incorporating model incompleteness, and a natural combination of economic modeling, forecasting and policy interventions.