midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series and Panel Data
The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
FAS: Testing for sparse idiosyncratic components in factor-augmented regression models
The 'FAS' package implements the bootstrap method for fully data-driven inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Beyhum & Striaukas (2024)
