FCVAR | R Documentation |

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

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

Returns `NULL`

. Object included for description only.

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.

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

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

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

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.

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