期刊名称：Discussion Paper Series / Department of Economics, New York University
出版年度：2009
卷号：1
出版社：New York University
摘要：We consider a general class of nonlinear optimal policy problems involv-
ing forward-looking constraints (such as the Euler equations that are typically
present as structural equations in DSGE models), and show that it is possible,
under regularity conditions that are straightforward to check, to derive a prob-
lem with linear constraints and a quadratic objective that approximates the ex-
act problem. The LQ approximate problem is computationally simple to solve,
even in the case of moderately large state spaces and ¡ãexibly parameterized
disturbance processes, and its solution represents a local linear approximation
to the optimal policy for the exact model in the case that stochastic distur-
bances are small enough. We derive the second-order conditions that must be
satis¡¥ed in order for the LQ problem to have a solution, and show that these
are stronger, in general, than those required for LQ problems without forward-
looking constraints. We also show how the same linear approximations to the
model structural equations and quadratic approximation to the exact welfare
measure can be used to correctly rank alternative simple policy rules, again in
the case of small enough shocks.
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