tarTest

Purpose

Estimates the p^th order threshold autoregression model.

Format

TARout = tarTest(yt, tar0)

Parameters:

• yt (Nx1 vector) – data.

• tar0 (struct) –

  TARControl structure containing the following elements:

  p	scalar, Autoregressive order of the STAR model.
  omit	scalar, Nx1 vector number of lags (below p) to omit from the matrix.
  lowerQuantile	scalar, the lower quantile.
  upperQuantile	scalar, the upper quantile.
  rep	scalar, the number of simulation replications.
  printOutput	scalar, 0 or 1, 1 prints output to the screen.
  graph	scalar, 0 or 1, 1 turns on plotting.
  dstart	scalar, start date of the time series in DT scalar format as used by plotTS.
  freq	scalar, Data frequency, 12 for monthly, 4 for quarterly or 1 for annual.

Returns:

TAROut (struct) –

TAROut structure containing the following return elements:

tests	vector of test statistics (in order): SupLM, ExpLM, AveLM, SupLMs, ExpLMs, AveLMs.
pvalues	vector, estimated asymptotic p-values or test statistics.
coefficients	matrix, first column contains estimated coefficients and second column contains standard errors.
regimeErrorVariance	vector, 2x1, error variance for Regime 1 and Regime 2, respectively.
thresholdLag	scalar, threshold variable lag.
thresholdValue	scalar, threshold estimate.
errorVariance	scalar, threshold model error variance.

Example

new;
cls;
library tsmt;

// Real GNP data
// Seasonally adjusted and transformed in annualized quarterly growth rates
// 1947-1990
gnp = loadd( getGAUSSHome() $+ "pkgs/tsmt/examples/gnp_4790.fmt");
yg = ln(gnp[., 1]);
y = (yg[2:rows(yg)]-yg[1:rows(yg)-1])*400;

// Declare the structure
struct TARControl tar0;


// Initialize the structure
tar0 = TARControlCreate();

// Maximum number of lags considered
tar0.p = 5;

// Lags to omit from the test
omit = { 3, 4 };
tar0.omit = omit;

// Number of replications for Monte Carlo
tar0.rep = 5000;

// Data start date and frequency
tar0.dstart = 1947;
tar0.freq = 4;

// Run function
struct TAROut tarfnl;
tarfnl = tarTest( y, tar0 );

References

1. Hansen, B.E. (1996). Inference when a nuisance parameter is nost identified under the null hypothesis, Econometrica, 64(2), 413-430.

2. Franses, P.H. and Dijk, D. (2000) Non-linear Time Series Models in Empirical Finance. Cambridge University Press, New York.

Library

tsmt

Source

tartest.src