# FCVARhypoTest: Test of Restrictions on FCVAR Model In FCVAR: Estimation and Inference for the Fractionally Cointegrated VAR

 FCVARhypoTest R Documentation

## Test of Restrictions on FCVAR Model

### Description

`FCVARhypoTest` performs a likelihood ratio test of the null hypothesis: "model is `modelR`" against the alternative hypothesis: "model is `modelUNR`".

### Usage

```FCVARhypoTest(modelUNR, modelR)
```

### Arguments

 `modelUNR` A list of estimation results created for the unrestricted model. `modelR` A list of estimation results created for the restricted model.

### Value

A list `LRtest` containing the test results, including the following parameters:

`loglikUNR`

The log-likelihood for the unrestricted model.

`loglikR`

The log-likelihood for the restricted model.

`df`

The degrees of freedom for the test.

`LRstat`

The likelihood ratio test statistic.

`p_LRtest`

The p-value for the likelihood ratio test.

The test is calculated using the results of two calls to `FCVARestn`, under the restricted and unrestricted models. Use `FCVARoptions` to set default estimation options for each model, then set restrictions as needed before `FCVARestn`.

Other FCVAR postestimation functions: `FCVARboot()`, `GetCharPolyRoots()`, `MVWNtest()`, `plot.FCVAR_roots()`, `summary.FCVAR_roots()`, `summary.MVWN_stats()`

### Examples

```
opt <- FCVARoptions()
opt\$gridSearch   <- 0 # Disable grid search in optimization.
opt\$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt\$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt\$constrained  <- 0 # Impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
m1 <- FCVARestn(x, k = 2, r = 1, opt)
opt1 <- opt
opt1\$R_psi <- matrix(c(1, 0), nrow = 1, ncol = 2)
opt1\$r_psi <- 1
m1r1 <- FCVARestn(x, k = 2, r = 1, opt1)
Hdb <- FCVARhypoTest(modelUNR = m1, modelR = m1r1)

opt1 <- opt
opt1\$R_Beta <- matrix(c(1, 0, 0), nrow = 1, ncol = 3)
m1r2 <- FCVARestn(x, k = 2, r = 1, opt1)
Hbeta1 <- FCVARhypoTest(m1, m1r2)

opt1 <- opt
opt1\$R_Alpha <- matrix(c(0, 1, 0), nrow = 1, ncol = 3)
m1r4 <- FCVARestn(x, k = 2, r = 1, opt1)
Halpha2 <- FCVARhypoTest(m1, m1r4)

```

FCVAR documentation built on May 5, 2022, 9:06 a.m.