Factor augmented VAR revisited: A sparse dynamic factor model approach

Arbeitspapier

We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings identify the unobserved factors and provide a meaningful economic interpretation for them. Given that we work with a general covariance matrix of factor innovations, we can implement different strategies for structural shock identification. Applying our methodology to US macroeconomic data (FRED QD) reveals indeed a high degree of sparsity in the data. The proposed identification procedure yields seven unobserved factors that account for about 52 percent of the variation in the data. We simultaneously identify a monetary policy, a productivity and a news shock by recursive ordering and by applying the method of maximizing the forecast error variance share in a specific variable. Factors and specific variables show sensible responses to the identified shocks.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 16.08

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Business Fluctuations; Cycles
• Subject: Bayesian FAVAR ; sparsity ; factor identification

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Beyeler, Simon ; Kaufmann, Sylvia
• Event: Veröffentlichung
• (who): Swiss National Bank, Study Center Gerzensee
• (where): Gerzensee
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/156502
• Last update: 10.03.2025, 11:43 AM CET