In this paper we evaluate the performance of three methods for testing the existence of a unit root in a time series, when the models under consideration in the null hypothesis do not display autocorrelation in the error term. In such cases, simple versions of the Dickey-Fuller test should be...

Prior studies have shown that automated variable selection results in models with substantially inflated estimates of the model R2, and that a large proportion of selected variables are truly noise variables. These earlier studies used simulated data sets whose sample sizes were at most 100. We...

This work shows a procedure that aims to eliminate or reduce the bias caused by omitted variables by means of the so-called regime-switching regressions. There is a bias estimation whenever the statistical (linear) model is under-specified, that is, when there are some omitted variables and they...

This paper proposes a wavelet-based approach to analyze spurious and cointegrated regressions in time series. The approach is based on the properties of the wavelet covariance and correlation in Monte Carlo studies of spurious and cointegrated regression. In the case of the spurious regression,...

In this paper, we propose a new augmented Dickey-Fuller-type test for unit roots which accounts for two structural breaks. We consider two different specifications: (a) two breaks in the level of a trending data series and (b) two breaks in the level and slope of a trending data series. The...