FCVAR {FCVAR}

A package for estimating the Fractionally Cointegrated VAR model.

Description

The FCVAR package estimates the Fractionally Cointegrated Vector Autoregressive (VAR) model. It includes functions for lag selection, cointegration rank selection and hypothesis testing.

Details

Functions in the FCVAR package are divided into four categories: Estimation, Postestimation, Specification and Auxiliary functions.

Value

Returns NULL. Object included for description only.

Estimation functions

The estimation functions include the primary estimation function FCVARestn and associated functions to set estimation options and display results. Some of these functions define, modify and test the user-specified options for estimation. FCVARoptions defines the default estimation options used in the FCVAR estimation procedure and the related programs. The user can then revise the options such as the settings for optimization and restrictions for testing hypotheses. After making these changes, an internal function FCVARoptionUpdates sets and tests estimation options for validity and compatibility.

Postestimation functions

The postestimation functions are used to display summary statistics, test hypotheses and test the goodness of fit of the estimated model. These include:

FCVARhypoTest

for a likelihood ratio test of a restricted vs. an unrestricted model

FCVARboot

for generating a distribution of a likelihood ratio test statistic

FCVARforecast

for calculating recursive forecasts with the FCVAR model

Specification functions

The specification functions are used to estimate a series of models in order to make model specfication decisions. These include:

FCVARlagSelect

for selection of the lag order

FCVARrankTests

for choosing the cointegrating rank

FCVARbootRank

for generating a distribution of a likelihood ratio test statistic for the rank test

Auxiliary functions

The auxiliary functions are used to perform intermediate calculations for estimation. These functions are mainly designed for use only within the estimation function. Some exceptions include:

FracDiff

for fractionally differencing a multivariate series

FCVARsimBS

for generating bootstrap samples from the FCVAR model

FCVARlikeGrid

for performing a grid-search optimization with the FCVAR likelihood function

Examples

A dataset votingJNP2014 is included for examples of the model building process. Sample model builds with hypothesis tests and examples of other extensions are found in the example script FCVAR_demo_JNP2014.R. See FCVAR_README.pdf for details at

https://github.com/LeeMorinUCF/FCVAR/blob/master/FCVAR_README.pdf

and also see https://sites.google.com/view/mortennielsen/software for more information about estimating the FCVAR model.