[R] [R-pkgs] new package 'midasr'

Vaidotas Zemlys zemlys at gmail.com
Sat Oct 12 12:44:43 CEST 2013

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

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