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Dóra Kocsis
dynare
Commits
27dbe4a9
Commit
27dbe4a9
authored
Dec 04, 2013
by
Stéphane Adjemian
Browse files
Closes #361. Document the fact that MCMCs are deterministic in Dynare.
parent
a79d24fb
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27dbe4a9
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...
@@ -4314,23 +4314,45 @@ acceptance ratio should be close to one third or one quarter. If this
not
the
case
,
you
can
stop
the
MCMC
(@
code
{
Ctrl
-
C
})
and
change
the
value
of
option
@
code
{
mh_jscale
}
(
see
below
).
Note
that
by
default
Dynare
generates
random
numbers
using
the
algorithm
@
code
{
mt199937ar
}
(@
i
{
ie
}
Mersenne
Twister
method
)
with
a
seed
set
equal
to
@
code
{
0
}.
Consequently
the
MCMCs
in
Dynare
are
deterministic
:
one
will
get
exactly
the
same
results
across
different
Dynare
runs
(@
i
{
ceteris
paribus
}).
For
instance
,
the
posterior
moments
or
posterior
densities
will
be
exactly
the
same
.
This
behaviour
allows
to
easily
identify
the
consequences
of
a
change
on
the
model
,
the
priors
or
the
estimation
options
.
But
one
may
also
want
to
check
that
across
multiple
runs
,
with
different
sequences
of
proposals
,
the
returned
results
are
almost
identical
.
This
should
be
true
if
the
number
of
iterations
(@
i
{
ie
}
the
value
of
@
code
{
mh_replic
})
is
important
enough
to
ensure
the
convergence
of
the
MCMC
to
its
ergodic
distribution
.
In
this
case
the
default
behaviour
of
the
random
number
generators
in
not
wanted
,
and
the
user
should
set
the
seed
according
to
the
system
clock
before
the
estimation
command
using
the
following
command
:
@
example
set_dynare_seed
(
'clock'
);
@
end
example
@
noindent
so
that
the
sequence
of
proposals
will
be
different
across
different
runs
.
@
algorithmshead
The
Monte
Carlo
Markov
Chain
(
MCMC
)
diagnostics
are
generated
by
the
estimation
command
if
@
ref
{
mh_replic
}
is
larger
than
2000
and
if
option
@
ref
{
nodiagnostic
}
is
not
used
.
If
@
ref
{
mh_nblocks
}
is
equal
to
one
,
the
convergence
diagnostics
of
@
cite
{
Geweke
(
1992
,
1999
)}
is
computed
.
It
uses
a
chi
square
test
to
compare
the
means
of
the
first
and
last
draws
specified
by
@
ref
{
geweke_interval
}
after
discarding
the
burnin
of
@
ref
{
mh_drop
}.
The
test
is
computed
using
variance
estimates
under
the
assumption
of
no
serial
correlation
as
well
as
using
tapering
windows
specified
in
@
ref
{
taper_steps
}.
If
@
ref
{
mh_nblocks
}
is
larger
than
1
,
the
convergence
diagnostics
of
@
cite
{
Brooks
and
Gelman
(
1998
)}
are
used
instead
.
As
described
in
section
3
of
@
cite
{
Brooks
and
Gelman
(
1998
)}
the
univariate
convergence
diagnostics
are
based
on
comparing
pooled
and
within
MCMC
moments
(
Dynare
displays
the
second
and
third
order
moments
,
and
the
length
of
the
Highest
Probability
Density
interval
covering
80
%
of
the
posterior
distribution
).
Due
to
computational
reasons
,
the
The
Monte
Carlo
Markov
Chain
(
MCMC
)
diagnostics
are
generated
by
the
estimation
command
if
@
ref
{
mh_replic
}
is
larger
than
2000
and
if
option
@
ref
{
nodiagnostic
}
is
not
used
.
If
@
ref
{
mh_nblocks
}
is
equal
to
one
,
the
convergence
diagnostics
of
@
cite
{
Geweke
(
1992
,
1999
)}
is
computed
.
It
uses
a
chi
square
test
to
compare
the
means
of
the
first
and
last
draws
specified
by
@
ref
{
geweke_interval
}
after
discarding
the
burnin
of
@
ref
{
mh_drop
}.
The
test
is
computed
using
variance
estimates
under
the
assumption
of
no
serial
correlation
as
well
as
using
tapering
windows
specified
in
@
ref
{
taper_steps
}.
If
@
ref
{
mh_nblocks
}
is
larger
than
1
,
the
convergence
diagnostics
of
@
cite
{
Brooks
and
Gelman
(
1998
)}
are
used
instead
.
As
described
in
section
3
of
@
cite
{
Brooks
and
Gelman
(
1998
)}
the
univariate
convergence
diagnostics
are
based
on
comparing
pooled
and
within
MCMC
moments
(
Dynare
displays
the
second
and
third
order
moments
,
and
the
length
of
the
Highest
Probability
Density
interval
covering
80
%
of
the
posterior
distribution
).
Due
to
computational
reasons
,
the
multivariate
convergence
diagnostic
does
not
follow
@
cite
{
Brooks
and
Gelman
(
1998
)}
strictly
,
but
rather
applies
their
idea
for
univariate
convergence
diagnostics
to
the
range
of
the
posterior
likelihood
...
...
@@ -10597,7 +10619,7 @@ Dynare).
@item example3.mod
A small RBC model in a stochastic setup, presented in
@cite{Collard (2001)}. The steady state is solved analytically using the @code{steady_state_model} block (@pxref{steady_state_model}).
@cite{Collard (2001)}. The steady state is solved analytically using the @code{steady_state_model} block (@pxref{steady_state_model}).
@item fs2000.mod
A cash in advance model, estimated by @cite{Schorfheide (2000)}. The file shows how to use Dynare for estimation.
...
...
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