Dear list members,

A new package, called *midasr* (version 0.1), is now available on 
CRAN: http://cran.r-project.org/package=midasr

This package provides econometric methods for working with mixed frequency 
data. The package provides tools for estimating the time series MIDAS 
regression, where the 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 non-linear least squares.
   * mls -- time series embedding to lower frequency, flexible function for 
specifying MIDAS models.
   * 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 lag, normalized beta lag and etc.

The package has the project webpage (http://mpiktas.github.io/midasr/) and you 
can follow its development on github (https://github.com/mpiktas/midasr).

The detailed description of the package features can be found in the user 
guide. The user guide, all of the code examples in the user guide and some 
additional examples together with the user guide .Rnw file can be found in the 
midasr-user-guide github repository 
(https://github.com/mpiktas/midasr-user-guide/).

Comments and suggestions would be greatly appreciated.

Vaidotas Zemlys
http://mif.vu.lt/~zemlys

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