Erschienen:
Cambridge, Mass: National Bureau of Economic Research, December 2003
Erschienen in:NBER working paper series ; no. w10194
Umfang:
1 Online-Ressource
Sprache:
Englisch
DOI:
10.3386/w10194
Identifikator:
Reproduktionsnotiz:
Hardcopy version available to institutional subscribers
Entstehung:
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Beschreibung:
We develop analytic asymptotic methods to characterize time series properties of nonlinear dynamic stochastic models. We focus on a stochastic growth model which is representative of the models underlying much of modern macroeconomics. Taking limits as the stochastic shocks become small, we derive a functional central limit theorem, a large deviation principle, and a moderate deviation principle. These allow us to calculate analytically the asymptotic distribution of the capital stock, and to obtain bounds on the probability that the log of the capital stock will differ from its deterministic steady state level by a given amount. This latter result can be applied to characterize the probability and frequency of large business cycles. We then illustrate our theoretical results through some simulations. We find that our results do a good job of characterizing the model economy, both in terms of its average behavior and its occasional large cyclical fluctuations