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Dynare has tools to compute optimal policies for various types of
objectives. You can either solve for optimal policy under commitment
with ramsey_policy
, for optimal policy under discretion with
discretionary_policy
or for optimal simple rule with
osr
.
Description
This command computes optimal simple policy rules for linear-quadratic problems of the form:
such that:
where:
The parameters to be optimized must be listed with osr_params
.
The quadratic objectives must be listed with optim_weights
.
This problem is solved using a numerical optimizer.
Options
This command accept the same options than stoch_simul
(see section Computing the stochastic solution).
The value of the objective is stored in the variable
oo_.osr.objective_function
, which is described below.
This command declares parameters to be optimized by osr
.
This block specifies quadratic objectives for optimal policy problems
More precisely, this block specifies the nonzero elements of the
quadratic weight matrices for the objectives in osr
.
A element of the diagonal of the weight matrix is given by a line of the form:
VARIABLE_NAME EXPRESSION; |
An off-the-diagonal element of the weight matrix is given by a line of the form:
VARIABLE_NAME, VARIABLE_NAME EXPRESSION; |
After an execution of the osr
command, this variable contains
the value of the objective under optimal policy.
Description
This command computes the first order approximation of the policy that maximizes the policy maker objective function submitted to the constraints provided by the equilibrium path of the economy.
The planner objective must be declared with the planner_objective
command.
See section Auxiliary variables, for an explanation of how this operator is handled internally and how this affects the output.
Options
This command accepts all options of stoch_simul
, plus:
planner_discount = EXPRESSION
Declares the discount factor of the central planner. Default: 1.0
instruments = (VARIABLE_NAME,…)
Declares instrument variables for the computation of the steady state
under optimal policy. Requires a steady_state_model
block or a
…_steadystate.m
file. See below.
Note that only first order approximation is available (i.e.
order=1
must be specified).
Output
This command generates all the output variables of stoch_simul
.
In addition, it stores the value of planner objective function under
Ramsey policy in oo_.planner_objective_value
.
Steay state
Dynare takes advantage of the fact that the Lagrange multipliers appear
linearly in the equations of the steady state of the model under optimal
policy. Nevertheless, it is in general very difficult to compute the
steady state with simply a numerical guess in initval
for the
endogenous variables.
It greatly facilitates the computation, if the user provides an
analytical solution for the steady state (in steady_state_model
block or in a …_steadystate.m
file). In this case, it is
necessary to provide a steady state solution CONDITIONAL on the value of
the instruments in the optimal policy problem and declared with option
instruments
. Note that choosing the instruments is partly a
matter of interpretation and you can choose instruments that are handy
from a mathematical point of view but different from the instruments you
would refer to in the analysis of the paper. Typical example is choosing
inflation or nominal interest rate as an instrument.
Description
This command computes an approximation of the optimal policy under discretion. The algorithm implemented is essentially an LQ solver, and is described by Dennis (2007).
Options
This command accepts the same options than ramsey_policy
, plus:
discretionary_tol = NON-NEGATIVE DOUBLE
Sets the tolerance level used to assess convergence of the solution
algorithm. Default: 1e-7
.
This command declares the policy maker objective, for use with
ramsey_policy
or discretionary_policy
.
You need to give the one-period objective, not the discounted lifetime
objective. The discount factor is given by the planner_discount
option of ramsey_policy
and discretionary_policy
.
Note that with this command you are not limited to quadratic objectives: you can give any arbitrary nonlinear expression.
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