Signal Extraction from Panel Data via Bayesian Sparse Regression and Spectral Decomposition

Provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) , empirical mode decomposition for non-stationary signals, Huang et al. (1998) , and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) . Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) , for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.