ecmFit#

Purpose#

Calculate and return parameter estimates for an error correction model.

Format#

vmo = ecmFit(y, p[, vmc])#
vmo = ecmFit(dataset, formula, p[, vmc])
Parameters:

  • y (Nx1 vector) – data.

  • dataset (string) – name of data set or null string.

  • formula (string) – formula string of the model. E.g. “y ~ X1 + X2” ‘y’ is the name of dependent variable, ‘X1’ and ‘X2’ are names of independent variables; E.g. “y ~ .” , ‘.’ means including all variables except dependent variable ‘y’;

  • p (scalar) – order of AR process.

  • vmc (struct) –

    Optional input, an instance of a varmamtControl structure. The following members of vmc are referenced within this routine:

    vmc.rho

    scalar, number of cointegrating relations. Set to -1 to have GAUSS estimate this value. Default = 0.

    vmc.header

    string, specifies the format for the output header. vmc.header can contain zero or more of the following characters:

    t

    title is to be printed.

    l

    lines are to bracket the title.

    d

    a date and time is to be printed.

    v

    version number of program is to be printed.

    f

    file name being analyzed is to be printed.

    Example:

    vmc .header = "tld";

    If vmc.header = “”, no header is printed. Default = “tldvf”.

    vmc.indEquations

    KxL matrix of zeros and ones. Used to set zero restrictions on the x variables to be estimated. Used only if the number of equations, vmc.L, is greater than one. Elements set to one indicate the coefficients to be estimated. If vmc.L = 1, all coefficients will be estimated. If vmc.L > 1 and vmc.indEquations is set to a missing value (the default), all coefficients will be estimated.

    vmc.lags

    scalar, number of lags over which ACF and Diagnostics are calculated. Default = 12.

    vmc.start

    Instance of a PV structure containing starting values. See VES-Starting Values for an example.

    vmc.nodet

    scalar. Set vmc.nodet = 1 to suppress the constant term from the fitted regression and include it in the co-integrating regression; otherwise, set vmc.nodet = 0. Default = 0.

    vmc.nwtrunc

    scalar, the number of autocorrelations to use in calculating the Newey-West correction. If vmc.nwtrunc = 0, GAUSS will use a truncation lag given by Newey and West, vmc.nwtrunc \(= 4(T/100)^{2/9}\).

    vmc.ctl

    An instance of an sqpsolvemtControl structure.

    vmc.ctl.covType

    scalar, if 2, QML standard errors are computed, if 0, none; otherwise Wald-type.

    vmc.ctl.printIters

    scalar, iteration information printed every swc.ctl.printIters-th iteration.

    See documentation for sqpsolvemtControl for further information regarding members of this structure.

    vmc.olsqtol

    scalar, the tolerance used in determining if diagonal elements are approaching zero in olsqrmt. Default = 1e-14.

    vmc.output

    scalar, if nonzero, results are printed to screen. Default = 1.

    vmc.row

    scalar. Specifies how many rows of the dataset are to be read per iteration of the read loop. By default, the number of rows to be read is calculated by ecmFit.

    vmc.scale

    scalar or an Lx1 vector, scales for the time series. If scalar, all series are multiplied by the value. If an Lx1 vector, each series is multiplied by the corresponding element of vmc.scale. Defa ult = 4/standard deviation (found to be best by e xperimentation).

    vmc.setConstraints

    scalar, set to a nonzero value to impose stationarity and invertibility by constraining roots of the AR and MA characteristic equations to be outside the unit circle. Set to zero to estimate an unconstrained model. Default = 1.

    vmc.title

    string, a title to be printed at the top of the output header (see vmc.header). By default, no title is printed ( vmc.title = “”).

Returns:

vmo (struct) –

An instance of a varmamOut structure containing the following members:

vmo.aa

Lxr matrix of coefficients, such that \(aa*bb=\Pi\) (see remarks below).

vmo.acfm

Lx(p*L) matrix, the autocorrelaton function. The first L columns are the lag l ACF; the last L columns are the lag p ACF.

vmo.aic

Lx1 vector, the Akaike Information Criterion.

vmo.arroots

px1 vector of AR roots, possibly complex.

vmo.bb

rxL matrix, eigenvectors spanning the cointegrating space of dimension r.

vmo.bic

Lx1 vector, the Schwarz Bayesian Information Criterion.

vmo.covpar

QxQ matrix of estimated parameters where Q is the number of estimated parameters. The parameters are in the row-major order: \(\Pi\), \(AR(1)\) to \(AR(p)\), beta (if x variables were present in the estimation), and the constants.

vmo.fct

Lx1 vector, the likelihood value.

vmo.lagr

An instance of an sqpsolvemtLagrange structure containing the following members:

vmo.lagr.lineq

linear equality constraints.

vmo.lagr.nlineq

nonlinear equality constraints.

vmo.lagr.linineq

linear inequality constraints.

vmo.lagr.nlinineq

nonlinear inequality constraints.

vmo.lagr.bounds

bounds.

When an inequality or bounds constraint is active, its associated Lagrangean is nonzero. The linear Lagrangeans precede the nonlinear Lagrangeans in the covariance matrices.

vmo.lrs

Lx1 vector, the likelihood ratio statistic.

vmo.maroots

qx1 vector of MA roots, possibly complex.

vmo.pacfm

Lxp*L matrix, the partial autocorrelation function, computed only if a univariate model is estimated. The first L columns are the lag 1 ACF; the last L columns are the lag p ACF.

vmo.par

An instance of a PV structure containing the parameter estimates, which can be retrieved using pvUnpack.

For example,

struct varmamtOut vout;
vout = varmaFit(y, 2);
ph = pvUnpack(v out.par, "zeta");
th = pvUnpack (vout.par, "pi");
vc = pvUnpack (vout.par, "vc");

The complete set of parameter matrices and arrays that can be unpacked depending on the model is:

phi

Lxpxp array, autoregression coefficients.

theta

Lxqxq array, moving average coefficients.

vc

LxL residual covariance matrix.

beta

LxK regression coefficient matrix.

beta0

Lx1 constant vector.

zeta

Lxpxar array of ecm coefficients.

pi

LxL matrix. Note that ‘pi’ is a reserved word in GAUSS. Users will need to assign this to a different variable name.

vmo.portman

vmc.lags-(p+q)x3 matrix of portmanteau statistics for the multivariate model and Ljung-Box statistics for the univariate model. The time period is in column one, the Qs (portmanteau) statistic in column two and the p_value in column three.

vmo.residuals

TxL matrix, residuals.

vmo.retcode

2x1 vector, return code. First element:

0:

normal convergence.

1:

forced exit.

2:

maximum number of iterations exceeded.

3:

function calculation failed.

4:

gradient calculation failed.

5:

Hessian calculation failed.

6:

line search failed.

7:

error with constraints.

Second element

0:

covariance matrix of parameters failed.

1:

ML covariance matrix.

2:

QML covariance matrix.

3:

Cross-Product covariance matrix.

vmo.ss

Lx2 matrix, the sum of squares for Y in column one and the sum of squared error in column two.

vmo.va

rx1 vector, eigenvalues.

Example#

new;
cls,;
library tsmt;

// Load data
fname = getGAUSSHome() $+ "pkgs/tsmt/examples/ecmmt.csv";
y = csvReadM(fname, 1, 2);

y = vmdiffmt(y, 1);

// Declare varmamt control structure
struct varmamtControl vmc;

// Initialize control structure with default values
vmc = varmamtControlCreate;

// No contraints
vmc.setConstraints = 0;

// Set up start values
phi = { 0.05 -0.05, 0 0.01, 0.1 -0.07, 0.05 -0.04 };
vmc.start = pvcreate();
vmc.start = pvPacki(vmc.start,areshape(phi, 2|2|2), "phi", 1);
vmc.start = pvPacksi(vmc.start, xpnd(15.9521|14.2525|15.9908), "vc", 3);

// Call ecmFit
struct varmamtOut vout;
vout = ecmFit(y , 1, vmc);

Remarks#

Errors are assumed to be distributed \(N(0, Q)\).

Library#

tsmt

Source#

varmamt.src

See also

Functions varmaFit()

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