Signal Extraction from Panel Data via Bayesian Sparse Regression and Spectral Decomposition
Apply Function to Matrix Columns
Create Block Bootstrap Samples
Check Stationarity of AR Coefficients
Compute Shannon Entropy
Compute LOO-CV for Horseshoe Model
Compute Partial R-squared
Create Summary Data Frame
Dynamic Factor Model Estimation
Evaluate Fit Quality
Extract MCMC Diagnostics
Apply Multiple Filters to a Series
Empirical Mode Decomposition Filter
Grant-Chan Embedded Hodrick-Prescott Filter
Wavelet Multiresolution Analysis Filter
Signal Filtering Methods for Trend Extraction
Fit Regularized Horseshoe Regression Model
Format Numeric Values for Display
Generate Automated Technical Interpretation
Get Stan Code for Regularized Horseshoe
Regularized Horseshoe Regression for Variable Selection
Linear Interpolation for Missing Values
Interpret ADF Test Results
Interpret ERS P-test Results
Interpret ERS DF-GLS Results
Interpret KPSS Results
Interpret Phillips-Perron Results
Interactive Plot for Signal Analysis
Principal Component Analysis with Bootstrap Significance Testing
Principal Component Analysis and Dynamic Factor Models
Plot Method for signal_analysis Objects
Posterior Predictive Check for Horseshoe Model
Print Horseshoe Summary
Print Unit Root Results
Print Method for signal_analysis Objects
Process Horseshoe Results
Procrustes Rotation for Bootstrap Alignment
Safe Division with Zero Handling
Select Variables Based on Credible Intervals
Select Variables Based on Effect Magnitude
Select Variables Based on Shrinkage
Comprehensive Signal Analysis for Panel Data
SignalY: Signal Extraction from Panel Data via Bayesian Sparse Regress...
Summary Method for signal_analysis Objects
Synthesize Unit Root Results
Comprehensive Unit Root Test Suite
Unit Root and Stationarity Tests
Utility Functions for SignalY
Validate Input Data Structure
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) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, 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.