Econometrics Seminar - Mengheng Li (UTS)

Title: Is dimensionality reduction a curse? Bayesian analysis of the mean-volatility dynamic factor model
Abstract: The factor stochastic volatility (FSV) model is a powerful dimensionality reduction device and has gained much popularity due to its parsimonious structure in modelling both time-varying mean and covariance matrix for multivariate time series. We document the failure of FSV models by observing a strong common volatility component left in the residuals, irrespective of the chosen number of factors. To adequately model co-movement in mean and volatility, we introduce the mean-volatility dynamic factor model which assumes separate factor structures for the first and the second moment of a high-dimensional vector time series. We identify and extract the mean factors and volatility factors via a Bayesian variable selection technique that pins down zeros in associated loading matrices and thus the factor space. Such identification scheme is order-invariant and gives economically meaningful factors. We also propose a computationally efficient multi-move sampler that samples all volatility series in parallel to speed up estimation. In the empirical study, we fit the model to the Fred-MD data of 128 monthly macroeconomic variables and find 18 mean factors and 8 volatility factors.