This paper introduces the use of Bayesian Vector Autoregressions (BVAR) for economic forecasting, highlighting its advantages over traditional econometric models and other time series techniques. BVAR models are noted for their simplicity, low cost, and accuracy, generating forecasts that are comparable to those from more expensive models. The approach does not require judgmental adjustments, making it a scientific method that can be evaluated independently. It also provides a complete, multivariate probability distribution for future economic outcomes, which is more realistic than those from competing methods. The paper discusses the challenges of economic forecasting, including limited data, measurement errors, complex interactions, and the difficulty of testing economic theories. It emphasizes the uncertainty in understanding economic structures and the need for a Bayesian approach to better represent this uncertainty. The author compares the performance of BVAR models with traditional econometric models and other forecasting methods, demonstrating that BVAR models can outperform them in terms of forecast accuracy. The paper also presents a detailed description of the BVAR model, including the specification of prior distributions and the estimation process. It includes a simulation experiment to illustrate the benefits of the Bayesian approach over standard methods, showing that BVAR models can capture more information from a wide spectrum of economic data. The author then applies the BVAR model to a real-world forecasting experiment, using quarterly data from 1948 to 1979, and compares the results with forecasts from professional forecasters and other models. Finally, the paper compares the BVAR forecasts with those from three commercial forecasting services (Data Resources, Wharton EFA, and Chase Econometrics) over a five-year period. The comparison highlights the volatility and accuracy of BVAR forecasts, noting that while they may be more volatile than conventional forecasts, they are more scientifically sound and reproducible. The paper concludes by discussing the limitations of the comparison and the importance of considering the context and specific applications of forecasting models.This paper introduces the use of Bayesian Vector Autoregressions (BVAR) for economic forecasting, highlighting its advantages over traditional econometric models and other time series techniques. BVAR models are noted for their simplicity, low cost, and accuracy, generating forecasts that are comparable to those from more expensive models. The approach does not require judgmental adjustments, making it a scientific method that can be evaluated independently. It also provides a complete, multivariate probability distribution for future economic outcomes, which is more realistic than those from competing methods. The paper discusses the challenges of economic forecasting, including limited data, measurement errors, complex interactions, and the difficulty of testing economic theories. It emphasizes the uncertainty in understanding economic structures and the need for a Bayesian approach to better represent this uncertainty. The author compares the performance of BVAR models with traditional econometric models and other forecasting methods, demonstrating that BVAR models can outperform them in terms of forecast accuracy. The paper also presents a detailed description of the BVAR model, including the specification of prior distributions and the estimation process. It includes a simulation experiment to illustrate the benefits of the Bayesian approach over standard methods, showing that BVAR models can capture more information from a wide spectrum of economic data. The author then applies the BVAR model to a real-world forecasting experiment, using quarterly data from 1948 to 1979, and compares the results with forecasts from professional forecasters and other models. Finally, the paper compares the BVAR forecasts with those from three commercial forecasting services (Data Resources, Wharton EFA, and Chase Econometrics) over a five-year period. The comparison highlights the volatility and accuracy of BVAR forecasts, noting that while they may be more volatile than conventional forecasts, they are more scientifically sound and reproducible. The paper concludes by discussing the limitations of the comparison and the importance of considering the context and specific applications of forecasting models.