Themes of interest are centered around scalable methods for high-dimensional dynamic econometrics; that is, high-dimensional aspects of selection, shrinkage (toward sparsity, toward reduced rank, ...), identification schemes for variance decompositions and impulse responses, summarization and visualization, optimal filtering, time-varying parameters, mixed-frequency and missing data, real-time vintage data, etc.

They include but are not limited to:

Dynamic modeling (e.g., VAR’s) in high dimensions (estimation, identification, parameter breaks, parameter evolution, stochastic volatility, interaction of time-varying parameters and stochastic volatility, ...); static and dynamic network connectedness measurement; static and dynamic network visualization.

Regularization including aspects of selection (all-subsets, partial-subsets, one-shot, ...); shrinkage (Bayesian and otherwise); distillation (factor structure, PCA, ...); hybrids (e.g., shrinkage toward factor structure, lasso and its variants, ...); situations with T < K.

Volatility including realized volatility in high dimensions (i.e., empirical quadratic variation and covariation), microstructure noise, jumps, “realized X” for various other X, ...); assembling and constraining “vast” realized covariance matrices; tick data and inter-trade durations; long memory and self-similarity; regime switching and multi-fractals.

• Aspects of all of the above in real-time monitoring contexts.