Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal Litterman prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag model is chosen. The results show that a...

I generate priors for a VAR from four competing models of economic fluctuations: a standard RBC model, Fisher’s (2006) investment-specific technology shocks model, an RBC model with capital adjustment costs and habit formation, and a sticky price model with an unaccommodating monetary...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal Litterman prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag model is chosen. The results show that a...

In this article the theoretical analysis and practical application of Bayesian approach for vector autoregressive model parameters estimation with different priors have been peformed. The time series was from 2001Q1 to 2010Q4 and included the following variables: GDP, CPI, exchange rate,...

We use factor augmented vector autoregressive models with time-varying coefficients to construct a financial conditions index. The time-variation in the parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for...