COINT (version 0.0.2)

fmvar_plag: Select the q in a FMVAR(p,q) by Specific Criterion

Description

Select the q for a FMVAR(p,q) for cointegrated regressions.

Usage

fmvar_plag(data,
          p=1,
          lag.max=12,
          v=15,
          type=c("const","trend","season","all"),
          ker_fun="parzen",
          aband=0,
          filter=0)

Value

selection

The selected order of q: the selected lagged innovation terms to include in the fitted FMVAR(p,q)

criteria

The matrix of all lags and the values of four criteria: "AIC(q)", "HQ(q)", "SC(q)", "FPE(q)".

Arguments

data

The dependent variables for a VAR system.

p

The number of lags for dependent variables, as in a standard VAR(p).

lag.max

The maxum number of lags used to search for optimal q in fmvar(p,q).

v

Number of autocovariance terms to compute the spectrum at frequency zero, default=15.

type

The deterministic parts in the regression.
const = intercept;
trend = lnear trend;
season = seasonal dummies, this option requires ts format;
all = all three options, which requires ts format.

ker_fun

Set kernel function to one of the available kernels, default="parzen". See section details below.

aband

Whether to activate the automatic bandwidth selection.
aband=1. To activate.
aband=0. Do not activate.The default.

filter

Whether to use an AR(1) filter to compute the spectrum at frequency zero.
filter=1. To use.
filter=0. Do not use. The default.

Author

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

Details

1. Available kernels. Technical details are referred to Brillinger (1981,P.55)
"parzen"=Parzen kernel
"bartlett"=Bartlett kernel
"dchlet"= Dirichlet kernel
"mdchlet"= Modified Dirichlet kernel
"tukham"=Tukey-Hamming kernel
"tukhan"=Tukey-Hanning kernel
"cauchy"=Cauchy kernel
"bohman"=Bohman kernel
"reisz"=Riesz,Bochner kernel
"gw"= Gauss-Weierstrass kernel
"qs"= Andrews (1991) Quadratic-Spectral

These kernels are written for fm.ols procedures, technically different from those used in pp and kpss tests.

2. Andrews (1991) has developed data based (or automatic) bandwidth procedures for computing the spectrum. COINT implements these procedures for the Parzen, Bartlette, Tukey-Hamming, and the Quadratic-Spectral kernels. When aband is active, COINT ignores the value you specify for the band-width parameter and automatically substitutes the data-based value.
3. The aim of the AR(1) filter is to flatten the spectrum of residual around the zero frequency, thereby making it easier to estimate the true spectrum by simple averaging of the periodogram.

References

Phillips, P. C. B (1995) Fully Modified Least Squares and Vector Autoregression. Econometrica, 63, 1023-1078.

Examples

data(macro)

Q=fmvar_plag(macro,
            p=1,
            v=15,
            lag.max=16,type="trend",
            ker_fun="parzen")$selection[1]

out=fmvar(macro,p=1,q=Q,v=15,type="trend", ker_fun="parzen")