We consider a stationary AR(1) process with ARCH(1) errors given by the stochastic difference equation $X_{t}=\alpha X_{t-1}+\sqrt{\beta +\lambda X_{t-1}^{2 ...

The regression model with autocorrelated disturbances is as follows: In these equations, y t are the dependent values, x t is a column vector of regressor variables, is a column vector of structural ...

When regression is performed on time series data, the errors may not be independent. Often errors are autocorrelated; that is, each error is correlated with the error ...

As spatial autocorrelation latent in georeferenced data increases, the amount of duplicate information contained in these data also increases. This property suggests the research question asking what ...