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Una regresión espuria es la presencia de una asociación entre variables inde- pendientes; y se presenta evidencia de ella cuando no se rechaza la hipótesis H0 : β1 = 0 al hacer dicha regresión. Desde la década de 1970 se había demostra- do que la regresión espuria es un fenómeno frecuente en caminatas aleatorias. Su identificación es de interés principalmente en la Econometría. En este traba jo se propone el empleo de una red neuronal artificial feedforward multicapa como herramienta de detección de regresión espuria entre caminatas aleatorias, siendo la pendiente cero de la respuesta de la red y la aceptación de la hipótesis sobre la media poblacional H 0 : δ1 = 0 en la respuesta de la red como variable dependiente de zt , un indicio de la no regresión entre las variables. Palabras clave: Redes neuronales artificiales, Regresiones espurias, Caminatas aleatorias, R, AMORE.__________A spurious regression is the asociation between independent variables; its evidence is found through rejecting H0 : β1 = 0 hipothesis in such regression. Since the 1970 decade it was shown that it is common for random walks. Econometrics has a particular interest in identify them. This work uses a multilayered feedforward artificial neural network as posible tool for detection of random walks spurious regression, having the zero slope of the network response and its acceptance of the hipothesis of the population mean H 0 : δ1 = 0 about the network response as dependent of zt , as an indicator of no-regression.

It has been found that the t-statistic for testing the null of no relationship between two independent variables diverges asymptotically under a wide variety of nonstationary data generating processes. This paper introduces a simple method which guarantees convergence of this t-statistic to a pivotal limit distribution, when there are drifts in the integrated processes generating the data, thus allowing asymptotic inference. This method can be used to distinguish a genuine relationship from a spurious one among integrated (I(1) and I(2)) processes. Simulation experiments show that the test has good properties in small samples. When applying the proposed procedure to real data (including the marriages and mortality data of Yule), we do not find (spurious) significant relationships between the variables.