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Bayesian estimation of sparse dynamic factor models with order-independent and ex-post mode identification

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  • Kaufmann, Sylvia
  • Schumacher, Christian

Abstract

Common variation in N series is extracted into k≪N dynamic factors. We induce sparsity by using a zero point mass–normal mixture prior distribution on the loadings. Estimation and rotational identification are independent of variable ordering. Sparsity helps identifying the factor space and the factors. Rotational identification, including factor order and sign, is obtained by processing the posterior output and based on factor draws rather than factor loading draws. Simulating data, we document sampler and estimation efficiency. To illustrate, we estimate the model for a large panel of Swiss macroeconomic and detailed price data. We identify 16 factors with a clear economic interpretation.

Suggested Citation

  • Kaufmann, Sylvia & Schumacher, Christian, 2019. "Bayesian estimation of sparse dynamic factor models with order-independent and ex-post mode identification," Journal of Econometrics, Elsevier, vol. 210(1), pages 116-134.
    • Handle: RePEc:eee:econom:v:210:y:2019:i:1:p:116-134
    DOI: 10.1016/j.jeconom.2018.11.008
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    More about this item

    Keywords

    Ex-post processing; Factor interpretation; Large dataset; Factor order permutation; Rotation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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