Autocorrelation problem causes unduly effects on the variance of Ordinary Least Squares (OLS) estimates. Hence, it is very essential to detect the autocorrelation problem so that appropriate remedial measures can be taken. The Breusch-Godfrey (BG) test is the most popular and commonly used test for the detection of autocorrelation. Since this test is based on the OLS estimates, which are not robust, it is easily affected by outliers. In this paper, we propose a robust Breusch-Godfrey (MBG) test which is not easily affected by outliers. The results of the study indicate that the MBG test is more powerful than the BG test in the detection of autocorrelation problem
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
It is evident from the comments by Bernoulli (1777) that the history of outliers is very old and tra...
Autocorrelated errors cause the Ordinary Least Squares (OLS) estimators to become inefficient. Hence...
In this article, we study the impact of an abrupt change in variance on the Breusch-Godfrey's LM tes...
The Breusch-Godfrey test for autocorrelated errors is generalised to cover systems of equations, and...
In regression analysis, autocorrelation of the error terms violates the ordinary least squares assum...
The Breusch-Godfrey’s LM test is one of the most popular tests for autocorrelation. However, it has...
We use Monte Carlo methods to study the properties of the bootstrap Breusch-Godfrey test for autocor...
Using Monte Carlo methods, the properties of systemwise generalisations of the BreauchGodfrey test f...
In this article, a nonparametric correlation coefficient is defined that is based on the principle o...
Under the assumption of that the variance-covariance matrix is fully populated, Baarda’s w-test is t...
In today’s society, statistical techniques are being used widely in education, medicine, social scie...
textThe present investigation was a Monte Carlo experiment designed to evaluate the performance of s...
International audienceWe are interested in the implications of a linearly autocorrelated driven nois...
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
It is evident from the comments by Bernoulli (1777) that the history of outliers is very old and tra...
Autocorrelated errors cause the Ordinary Least Squares (OLS) estimators to become inefficient. Hence...
In this article, we study the impact of an abrupt change in variance on the Breusch-Godfrey's LM tes...
The Breusch-Godfrey test for autocorrelated errors is generalised to cover systems of equations, and...
In regression analysis, autocorrelation of the error terms violates the ordinary least squares assum...
The Breusch-Godfrey’s LM test is one of the most popular tests for autocorrelation. However, it has...
We use Monte Carlo methods to study the properties of the bootstrap Breusch-Godfrey test for autocor...
Using Monte Carlo methods, the properties of systemwise generalisations of the BreauchGodfrey test f...
In this article, a nonparametric correlation coefficient is defined that is based on the principle o...
Under the assumption of that the variance-covariance matrix is fully populated, Baarda’s w-test is t...
In today’s society, statistical techniques are being used widely in education, medicine, social scie...
textThe present investigation was a Monte Carlo experiment designed to evaluate the performance of s...
International audienceWe are interested in the implications of a linearly autocorrelated driven nois...
The present paper focuses on the theory of outlier detection in least-squares adjustment. Although t...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
It is evident from the comments by Bernoulli (1777) that the history of outliers is very old and tra...