This paper considers the exact finite sample powers of five popular tests for AR(1) disturbances when one of several types of heteroscedasticity is also preseut. Severe reductions in power are found, particularly under strong positive autocorrelation. Factors influencing these power reductions are identified analytically and the limiting powers are also considered
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Symmetrical distributions, Nonlinear model, AR(1) errors, Heteroscedasticity, Score test, Asymptotic...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
Using the power kernels of Phillips, Sun, and Jin (2006, 2007), we examine the large sample asymptot...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
A comprehensive empirical examination is made of the sensitivity of tests of disturbance covariance ...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
We consider the power functions of five popular tests for AR(1) errors in a linear regression model ...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
We study the exact power of the Goldfeld-Quandt test in a linear regression model with errors which ...
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Symmetrical distributions, Nonlinear model, AR(1) errors, Heteroscedasticity, Score test, Asymptotic...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
Using the power kernels of Phillips, Sun, and Jin (2006, 2007), we examine the large sample asymptot...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
A comprehensive empirical examination is made of the sensitivity of tests of disturbance covariance ...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
We consider the power functions of five popular tests for AR(1) errors in a linear regression model ...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. W...
We study the exact power of the Goldfeld-Quandt test in a linear regression model with errors which ...
Testing restrictions on regression coefficients in linear models often requires correcting the conve...
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample ...
Symmetrical distributions, Nonlinear model, AR(1) errors, Heteroscedasticity, Score test, Asymptotic...