Change search
ReferencesLink to record
Permanent link

Direct link
A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.
Keyword [en]
Bayesian VAR, Gibbs Sampling, State-space, Mixed Frequency Data, Steady-state, Macroeconometrics, Forecasting
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-297406OAI: diva2:941742
Educational program
Master Programme in Statistics
Available from: 2016-06-27 Created: 2016-06-22 Last updated: 2016-06-27Bibliographically approved

Open Access in DiVA

fulltext(417 kB)14 downloads
File information
File name FULLTEXT01.pdfFile size 417 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 14 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 64 hits
ReferencesLink to record
Permanent link

Direct link