midasML - estimation and prediction for high-dimensional mixed frequency time series data.

The midasML package implements estimation and prediction methods for high dimensional time series regression models under mixed data sampling data structures using structured-sparsity penalties and orthogonal polynomials. For more information on the midasML approach see [1]. The package also allows to estimate and predict using single-variate MIDAS regressions. Note that such regressions are also implemented in `midasr`

package. Functions implemented in this package allows to directly compare low-dimensional and high-dimensional MIDAS regression models.

The core of the midasML method is the sparse-group LASSO (sg-LASSO) estimator proposed by [2], and studied for high-dimensional time series data by [1, 3]. The sg-LASSO consists of group structures that are present in high-dimensional ARDL-MIDAS model, hence it is a natural estimator for such model.

The main algorithm for solving sg-LASSO estimator is taken from [2].

Functions that compute MIDAS data structures were inspired by MIDAS Matlab toolbox (v2.3) written by Eric Ghysels, see [4].

`midasml_forecast`

- midasML estimation and prediction function.`midas_ardl`

- ARDL-MIDAS single-variate estimation and prediction function (accomodates different weight functions and loss functions, e.g. quantile regression loss).`midas_dl`

- DL-MIDAS single-variate estimation and prediction function (accomodates different weight functions and loss functions, e.g. quantile regression loss). ### Estimation only functions`reg_sgl`

- sg-LASSO regression estimation (currently only for mse loss).`panel_sgl`

- panel sg-LASSO regression estimation (currently only for mse loss). ### Data handling functions`qtarget.sort_midasml`

- transforms data into format suitable for midasML technique, creating in-sample and out-of-sample observations for quarterly target variable. Output could be directly inputed into`midasml_forecast`

(note: currently does not handle real-time data vintages. in case real-time experiment is considered for a specific application, this function can help to setup up the data for each quarter prediction separately. future updates will contain functions capable of handling real-time data vintages.)`mixed_freq_data`

transforms data into MIDAS regression format creating in-sample and out-of-sample observations. Output is subsequenlty used in`midas_ardl`

&`midas_dl`

[1] Babii, A., Ghysels, E., & Striaukas, J. (2020). Machine learning time series regressions with an application to nowcasting. https://arxiv.org/abs/2005.14057

[2] Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2013). A sparse-group lasso. Journal of computational and graphical statistics, 22(2), 231-245. Related CRAN R package. https://CRAN.R-project.org/package=SGL

[3] Babii, A., Ghysels, E., & Striaukas, J. (2020). Inference for high-dimensional regressions with heteroskedasticity and autocorrelation. https://arxiv.org/abs/1912.06307.

[4] Ghysels, E. et. al. Mathworks Matlab toolbox. https://www.mathworks.com/matlabcentral/fileexchange/45150-midas-matlab-toolbox