A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR model estimated on data aggregated to quarterly frequency and a quarterly least squares VAR with standard parametrization. Forecasts are evaluated using root mean squared errors and the log-determinant of the forecast error covariance matrix. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year. For quarterly variables the one and two quarter forecasts are improved when using monthly data.
Place, publisher, year, edition, pages
2016. , 30 p.
Bayesian VAR, Gibbs Sampling, State-space, Mixed Frequency Data, Steady-state, Macroeconometrics, Forecasting
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:uu:diva-297406OAI: oai:DiVA.org:uu-297406DiVA: diva2:941742
Master Programme in Statistics