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Durable Consumption and Asset Management with Transaction and Observation Costs
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1725-6704
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EUI ECO; 2010/04
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ALVAREZ, Fernando, GUISO, Luigi, LIPPI, Francesco, Durable Consumption and Asset Management with Transaction and Observation Costs, EUI ECO, 2010/04 - https://hdl.handle.net/1814/13224
Abstract
The empirical evidence on rational inattention lags far behind the theoretical developments:
micro evidence on the most immediate consequence of observation costs
- the infrequent observation of state variables - is not available in standard datasets.
We contribute to filling the gap with two novel household surveys that record the frequency
with which investors observe the value of their financial investments, as well as
the frequency with which they trade in financial assets and durable goods. We use these
data to test some predictions of existing models and show that to match the patterns
in the data we need to modify these models by shifting the focus from non-durable to
durable consumption. The model we develop features both observation and transaction
costs and implies a mixture of time-dependent and state-dependent rules, where the
importance of each rule depends on the ratio of the observation to the transaction cost.
Numerical simulations show that the model can produce frequency of portfolio observations
and asset trading comparable to that of the median investor (about 4 and 0.4 per
year, respectively) with small observation costs (about 1 basis point of financial wealth)
and larger transaction costs (about 30 basis points of financial wealth). In spite of its
small size the observation cost gives rise to infrequent information gathering (between
monthly and quarterly). A quantitative assessment of the relevance of the observation
costs shows that the behavior of the investors is essentially unchanged compared to the
one produced by a model with transaction but no observation cost. We test a novel
prediction of the model on the relationship between assets trades and durable-goods
trades and find that it is aligned with the data