Spurious regression phenomenon has been recognized for a wide range of Data Generating Processes: driftless unit roots …

the dependent and explanatory variables in a linear spurious regression model. We study cases when the nonstationarity in …, and show that the spurious regression phenomenon is present in all cases considered, when at least one of the variables …

the dependent and explanatory variables in a linear spurious regression model. We study cases when the nonstationarity in …, and show that the spurious regression phenomenon is present in all cases considered, when at least one of the variables …

In this paper, we propose the use of wavelet covariance and correlation to detect spurious regression. Based on Monte … a spurious regression between bivariate time series if the wavelet covariance and correlation for the two series are … test. Another distinct advantage of the graphical wavelet analysis of wavelet covariance and correlation to detect spurious …

A “spurious regression” is one in which the time-series variables are non-stationary and independent. It is well … some common tests for the Normality and homoskedasticity of the errors in a spurious regression. …

This paper analyzes a spurious regression involving AR(p) infinite-variance processes in the presence of structural … spurious regression not only depend on the autoregressive parameter and tailed index, but are sensitive to the presence of a …

regression occurs. However, if we re-estimate the regression by adding an AR(1) term, spurious regression can almost be … autoregression models. Our simulations show that when these time series data are used in ordinary least squares regression, spurious …