Conditional Means of Time Series Processes and Time Series Processes for Conditional Means
The authors study the process for the conditional mean of vector linear processes, which nest many models of interest. They also consider the joint process for a variable and its mean conditional on a multivariate information set. The authors compare the persistence of shocks to stationary variables and their means using impulse response functions. An empirical application suggests that U.S. real stock returns are close to white noise, while expected returns follow an AR(1) with high autocorrelation. The authors also find that unexpected variations in expected returns immediately produce large negative observed returns, thereafter compensated by slowly diminishing increments on expected returns. Copyright 1998 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
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Volume (Year): 39 (1998)
Issue (Month): 4 (November)
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