Mixed Data Sampling Regression.

The midasr R package provides econometric methods for working with mixed frequency data. The package provides tools for estimating time series MIDAS regression, where response and explanatory variables are of different frequency, e.g. quarterly vs monthly. The fitted regression model can be tested for adequacy and then used for forecasting. More specifically, the following main functions are available:

• midas_r -- MIDAS regression estimation using NLS.
• midas_nlpr -- Non-linear parametric MIDAS regression estimation.
• midas_sp -- Semi-parametric and partialy linear MIDAS regression.
• midas_qr -- Quantile MIDAS regression.
• mls -- time series embedding to lower frequency, flexible function for specifying MIDAS models.
• mlsd -- time series embedding to lower frequency using available date information.
• hAh.test and hAhr.test -- adequacy testing of MIDAS regression.
• forecast -- forecasting MIDAS regression.
• midasr_ic_table -- lag selection using information criteria.
• average_forecast -- calculate weighted forecast combination.
• select_and_forecast -- perform model selection and then use the selected model for forecasting.

The package provides the usual methods for generic functions which can be used on fitted MIDAS regression object: summary, coef, residuals, deviance, fitted, predict, logLik. It also has additional methods for estimating robust standard errors: estfun and bread.

The package also provides all the popular MIDAS regression restrictions such as normalized Almon exponential, normalized beta and etc.

The package development was influenced by features of the MIDAS Matlab toolbox created by Eric Ghysels.

The package has the project webpage and you can follow its development on github.

The detailed description of the package features can be found in the JSS article.

Development

The stable versions of the package have version numbers x.y. All the stable versions are submitted to CRAN. The development versions have version numbers x.y.z.

To install the development version of midasr, it's easiest to use the devtools package:

# install.packages("devtools")
library(devtools)
install_github("midasr","mpiktas")