Abstract
ABSTRACT Serially correlated errors are most likely to appear in regression analysis when time-series data are used either as a true symptom of autocorrelation or as an indication of a false specification among variables. This study examines the problem of serially correlated errors in the context of spurious regression for two independent stationary AR(1) processes, showing evidence of removing the presence of both symptoms using a Monte Carlo analysis.
Published Version
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