Vector autoregression with varied frequency data
AbstractThe Vector Autoregression (VAR) model has been extensively applied in macroeconomics. A typical VAR requires its component variables being sampled at a uniformed frequency, regardless of the fact that some macro data are available monthly and some are only quarterly. Practitioners invariably align variables to the same frequency either by aggregation or imputation, regardless of information loss or noises gain. We study a VAR model with varied frequency data in a Bayesian context. Lower frequency (aggregated) data are essentially a linear combination of higher frequency (disaggregated) data. The observed aggregated data impose linear constraints on the autocorrelation structure of the latent disaggregated data. The perception of a constrained multivariate normal distribution is crucial to our Gibbs sampler. Furthermore, the Markov property of the VAR series enables a block Gibbs sampler, which performs faster for evenly aggregated data. Lastly, our approach is applied to two classic structural VAR analyses, one with long-run and the other with short-run identification constraints. These applications demonstrate that it is both feasible and sensible to use data of different frequencies in a new VAR model, the one that keeps the branding of the economic ideas underlying the structural VAR model but only makes minimum modification from a technical perspective.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 34682.
Date of creation: Oct 2010
Date of revision:
Vector Autoregression; Bayesian; Temporal aggregation;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
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