Vector Autoregression with Mixed Frequency Data

Qian, Hang (2013): Vector Autoregression with Mixed Frequency Data.

Abstract

Three new approaches are proposed to handle mixed frequency Vector Autoregression. The first is an explicit solution to the likelihood and posterior distribution. The second is a parsimonious, time-invariant and invertible state space form. The third is a parallel Gibbs sampler without forward filtering and backward sampling. The three methods are unified since all of them explore the fact that the mixed frequency observations impose linear constraints on the distribution of high frequency latent variables. By a simulation study, different approaches are compared and the parallel Gibbs sampler outperforms others. A financial application on the yield curve forecast is conducted using mixed frequency macro-finance data.

• Item Type: MPRA Paper
• Language: English
• Keywords: VAR, Temporal aggregation, State space, Parallel Gibbs sampler
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access

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