Informational Assumptions on Income Processes and Consumption in the Buffer Stock Model of Savings
Idiosyncratic household income is typically assumed to consist of several components. While the total income is observed and is often modelled as an integrated moving average process, individual components are not observed directly. In the literature, econometricians typically assume that household income is the sum of a random walk permanent component and a transitory component, with uncorrelated permanent and transitory shocks. This characterization is not innocuous since households may have better information on individual income components than econometricians do. I show that, for the same reduced form model of income, different models for the income components lead to sizeably different estimates of the marginal propensity to consume (MPC) out of shocks to current and lagged income, and the volatility of consumption changes relative to income changes in data generated by an infinite horizon buffer stock model. I further suggest that the MPC out of shocks to current and lagged income estimated from empirical micro data should help identify parameters of individual components of the income process, including the correlation between transitory and permanent shocks. I use the method of simulated moments (MSM) and data from the Panel Study of Income Dynamics (PSID) and the Consumer Expenditure Survey (CEX) to estimate a structural life cycle model of consumption. I also jointly estimate the parameters governing the income process. I find statistically significant negative contemporaneous correlation between permanent and transitory shocks to income and reasonable, precisely estimated values for the time discount factor and the relative risk aversion parameter
|Date of creation:||03 Dec 2006|
|Date of revision:|
|Contact details of provider:|| Postal: Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA|
Web page: http://www.EconomicDynamics.org/
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