Looking for the News in the Noise - Additional Stochastic Implications of Optimal Consumption Choice
Laurence Kotlikoff () and
Ariel Pakes ()
No 1492, NBER Working Papers from National Bureau of Economic Research, Inc
In neoclassical models of consumption choice under earnings uncertainty changes in consumption programs from one period to the next are determined by new information received about future earnings over the period. This proposition suggests testing the neoclassical model by ascertaining whether new earnings information explains consumption choice through time. It also suggests that actual consumption choices imbed extractable information about the extent and time resolution of earnings uncertainty. This paper derives a fairly general theoretical relationship between properly defined innnovations in consumption (noise) and revisions in expectations of lifetime earnings (news). It also clarifies the relationship between testing for the theoretical determinants of consumption and standard Euler tests that focus on theoretical nondeterminants of consumption. The chief prediction of the paper's theoretical results, that noise exactly equals news, is tested using aggregate time series data on consumption and earnings. We find that new earnings information explains only a very small fraction of the variance of aggregate consumption innovations. On the other hand, the extent of suboptimal consumption choice appears to be of little economic significance.
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Published as "Looking for the News in the Noise. Additional Stochastic Implications of Optimal Consumption Choice." From Annales D'Economie et de Statistique, Vol. 9, pp. 29-46, (1988).
Published as Laurence J. Kotlikoff & Ariél Pakes, 1988. "Looking for the News in the Noise. Additional Stochastic Implications of Optimal Consumption Choise," Annals of Economics and Statistics, GENES, issue 9, pages 29-46.
Published as Kotlikoff & Pakes, 1988. "Looking for the News in the Noise. Additional Stochastic Implications of Optimal Consumption Choice," Annales d'Économie et de Statistique, .
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