## Abstract

Conditional on knowing the number of factors, r, analysis in static and dynamic factor models is straightforward for the Bayesian. However, inference on r is challenging. A Bayesian could use marginal likelihoods to select the number of factors (see Geweke, 1996). But in the standard big data setups nowadays (which involve a large number of variables/measurements m), this is computationally cumbersome, requiring the estimation of a large set of models that vary in r

(≤ m).

(≤ m).

Original language | English |
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Number of pages | 8 |

Journal | Bayesian Analysis |

Publication status | Accepted/In press - 4 Jul 2024 |

## Keywords

- Bayesian analysis
- Bayesian factor analysis
- identification
- prior choice
- computation