We introduce the “sample” technique to generate robust critical values for the Jarque and Bera (JB) Lagrangian Multiplier (LM) test for normality, JBCV( 1 2 k ,k ), by using improved critical values the true size of the test approaches its nominal value. Monte Carlo methods are used to study...

The size and power of the Wald, Edgeworth expansion corrected likelihood-ratio statistic (LRE), and bootstrap tests for Granger causality in integrated-cointegrated VAR systems are considered. By using Monte Carlo methods and simple graphical techniques, the p-value plots, and power-size plots,...

By using bootstrap technique we investigate the properties of the Breusch [Breusch, T.S., 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers 17, 334-355]-Godfrey [Godfrey, L.G., 1978. Testing for higher order serial correlation in regression equations when the...

The size and power of the ECM cointegration test are investigated by using the 'bootstrap critical values.' The purpose of this paper is to show the ability of the bootstrap technique to produce critical values which are much more accurate than the asymptotic ones. The properties of the test...

The size and power of various generalization tests for the Granger-causality in integrated-cointegrated VAR systems are considered. By using Monte Carlo methods, properties of eight versions of the test are studied in two different forms, the standard form and the modified form by Dolado &...

In this paper, we introduce a set of critical values for unit root tests that are robust in the presence of conditional heteroscedasticity errors using the normalizing and variance-stabilizing transformation (NoVaS) in Politis (2007) and examine their properties using Monte Carlo methods. In...

Monte Carlo methods are used to study the size and the power of three versions of the Jarque and Bera Lagrangian multiplier test for normality, JB(g<SUB align="right"><SMALL>1</SMALL></SUB>, g<SUB align="right"><SMALL>2</SMALL></SUB>), JB(b<SUB align="right"><SMALL>1</SMALL></SUB>, b<SUB align="right"><SMALL>2</SMALL></SUB>) and, finally, JB(k<SUB align="right"><SMALL>1</SMALL></SUB>, k<SUB align="right"><SMALL>2</SMALL></SUB>). The difference between these tests comes from the different definitions (estimates) of sample skewness...</small></sub></small></sub></small></sub></small></sub></small></sub></small></sub>

In this paper, we investigate the properties of the Granger causality test in stationary and stable vector autoregressive models under the presence of spillover effects, that is, causality in variance. The Wald test and the WW test (the Wald test with White's proposed...

This paper investigates the effect of spillover (i.e., causality in variance) on the Johansen tests for cointegration by conducting a Monte Carlo experiment where 16 different data generating processes (DGP) are used and a number of factors that might affect the properties of the Johansen...