dynare issueshttps://git.dynare.org/Dynare/dynare/issues2019-11-14T17:38:13Zhttps://git.dynare.org/Dynare/dynare/issues/1389Check detrending engine2019-11-14T17:38:13ZJohannes Pfeifer Check detrending engineThe mod-file
```
//-----------------------------------------------------------------------//
//---------------------- Declaring parameters ---------------------------//
//-----------------------------------------------------------------------//
parameters delta //depreciation
sigma //intertemporal elasticity
beta //discount factor
alpha //production function parameter
mu //utility parameter
theta //Calvo parameter
epsilon //elasticity
chi //indexation parameter (unused for now)
;
alpha = 0.667;
delta = 0.1;
sigma = 0.25;
beta = 0.96;
mu = 0.2;
theta = 0.5;
epsilon = 15;
chi = 0;
//-----------------------------------------------------------------------//
//----------------------- Declaring variables ---------------------------//
//-----------------------------------------------------------------------//
varexo omega //probability of remaining a worker in the next period
gamma //probability of dieing (once retired)
n //populational growth
x //rate of technological change
M_d //exogenous money supply
;
var lambda //asset distribution in the economy
pi //}these define the marginal propensity of consumption
eps //}by both retirees and workers (I'm using Gertler's notation)
OMEGA //higher case omega
R //gross interest rate
PSI //auxiliar variable
mc //marginal cost
Pi //inflation
Df //nominal dividends
Pf //nominal firm share price
price_disp //price dispersion index
;
//declaring nonstationary variables
trend_var(growth_factor= (1+x)*(1+n)/(1+n)) X; //technological progress
trend_var(growth_factor= (1+x)*(1+n)/(1+x)) N; //population
var(deflator = X*N)
Y //product
C //consumption
K //financial capital
H //non-financial capital
A //assets
;
var(deflator = X) W; //real wage
var(deflator = 1/(X*N)) P PStar; //price level and optimal price set
var(deflator = (X*N)^(1-epsilon)) g1; //auxiliary Calvo variable
var(deflator = (X*N)^(2-epsilon)) g2; //auxiliary Calvo variable
predetermined_variables K; //timing convention
//-----------------------------------------------------------------------//
//------------------------------- Model ---------------------------------//
//-----------------------------------------------------------------------//
model;
// Consumer side
//1
K(+1) = Y - C + (1 - delta)* K;
//2
(lambda - (1 - omega(+1)))*A = omega(+1)*(1-eps*pi)*lambda(-1)*R*A(-1);
//3
pi = 1 - PSI(+1) * (R(+1) * OMEGA(+1))^(sigma - 1) * beta^sigma * pi/pi(+1);
//4
eps * pi = 1 - PSI(+1) * ((R(+1))^(sigma-1)*beta^sigma*gamma(+1))*(eps*pi)/(eps(+1)*pi(+1));
//5
OMEGA = omega + (1-omega)*eps^(1/(1-sigma));
//6
H = N * W + H(+1)/((1+n(+1))*(1+x(+1))*R(+1)*OMEGA(+1)/omega(+1));
//7
C * (1 + mu^sigma * (R(+1)*Pi(+1)/(R(+1)*Pi(+1)-1))^(sigma-1)) = pi * ((1 + (eps/gamma-1) * lambda(-1)) * R * A(-1) + H(-1));
//8
A = K + 1/R * M_d(+1)/P + Pf/P;
//9
PSI = (1 + ((R*Pi-1)/(R*Pi))^(sigma-1) * mu^sigma)^(-1) / (1 + (((R(+1)*Pi(+1))-1)/(R(+1)*Pi(+1)))^(sigma-1) * mu^sigma)^(-1);
// Firm side
//10
Y = (X * N)^alpha * (K)^(1-alpha)/price_disp;
//11
W = alpha * Y / N * mc;
//12
R = (1 - alpha) * Y / K * mc + 1 - delta;
//13
mc = (1/(1-alpha))^(1-alpha)*(1/alpha)^alpha*(W/X)^(1-alpha)*(R-(1-delta))^alpha;
// Calvo pricing
//14
PStar = epsilon/(epsilon-1) * g1/g2;
//15
g1 = P^epsilon * Y * mc + theta*beta * g1(+1);
//16
g2 = P^(epsilon-1) * Y + theta*beta * g2(+1);
//17
P = (theta * P(-1)^(1-epsilon) + (1-theta) * PStar^(1-epsilon))^(1/(1-epsilon));
//18
price_disp = theta*(PStar/P)^(-epsilon)*(P/P(-1))^(epsilon) + (1-theta)*(P/P(-1))^(epsilon)*price_disp(-1);
//19
Pi = P/P(-1);
// Dividends and share prices
Df = P * Y *(1-mc);
Pf(+1) + Df(+1) = R(+1) * Pf;
end;
//-----------------------------------------------------------------------//
//--------------------------- Initial Values ----------------------------//
//-----------------------------------------------------------------------//
initval;
M_d = 1; P =1;
x = 0.01;
n = 0.01;
omega = 0.97;
gamma = 0.9;
lambda =0.3878;
pi =0.2394;
eps =1.2832;
OMEGA =1.0124;
R =1.3968;
PSI =0.9988;
mc =0.9064;
Y =0.7407;
C =0.6857;
K =0.459;
H =1.4279;
A =1.365;
P =0.972;
PStar =0.9364;
W =0.448;
Pi =0.98;
Df =0.0681;
Pf =0.1732;
price_disp=1.0336;
g1 =0.601;
g2 =0.6877;
end;
model_diagnostics;
steady;
endval;
gamma = 0.94;
end;
steady;
simul(periods=300);
```
from http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=14206 does not run with
```
ERROR: the second-order cross partial of equation 14 w.r.t. trend variable X and endogenous variable PStar is not null.
```
but the relevant equation
```
PStar = epsilon/(epsilon-1) * g1/g2;
```
should have the trends specified (as far as I can see). `g1/g2=((X*N)^(1-epsilon))/((X*N)^(2-epsilon)=(XN)^(-1)`, which is the trend for `PStar`.The mod-file
```
//-----------------------------------------------------------------------//
//---------------------- Declaring parameters ---------------------------//
//-----------------------------------------------------------------------//
parameters delta //depreciation
sigma //intertemporal elasticity
beta //discount factor
alpha //production function parameter
mu //utility parameter
theta //Calvo parameter
epsilon //elasticity
chi //indexation parameter (unused for now)
;
alpha = 0.667;
delta = 0.1;
sigma = 0.25;
beta = 0.96;
mu = 0.2;
theta = 0.5;
epsilon = 15;
chi = 0;
//-----------------------------------------------------------------------//
//----------------------- Declaring variables ---------------------------//
//-----------------------------------------------------------------------//
varexo omega //probability of remaining a worker in the next period
gamma //probability of dieing (once retired)
n //populational growth
x //rate of technological change
M_d //exogenous money supply
;
var lambda //asset distribution in the economy
pi //}these define the marginal propensity of consumption
eps //}by both retirees and workers (I'm using Gertler's notation)
OMEGA //higher case omega
R //gross interest rate
PSI //auxiliar variable
mc //marginal cost
Pi //inflation
Df //nominal dividends
Pf //nominal firm share price
price_disp //price dispersion index
;
//declaring nonstationary variables
trend_var(growth_factor= (1+x)*(1+n)/(1+n)) X; //technological progress
trend_var(growth_factor= (1+x)*(1+n)/(1+x)) N; //population
var(deflator = X*N)
Y //product
C //consumption
K //financial capital
H //non-financial capital
A //assets
;
var(deflator = X) W; //real wage
var(deflator = 1/(X*N)) P PStar; //price level and optimal price set
var(deflator = (X*N)^(1-epsilon)) g1; //auxiliary Calvo variable
var(deflator = (X*N)^(2-epsilon)) g2; //auxiliary Calvo variable
predetermined_variables K; //timing convention
//-----------------------------------------------------------------------//
//------------------------------- Model ---------------------------------//
//-----------------------------------------------------------------------//
model;
// Consumer side
//1
K(+1) = Y - C + (1 - delta)* K;
//2
(lambda - (1 - omega(+1)))*A = omega(+1)*(1-eps*pi)*lambda(-1)*R*A(-1);
//3
pi = 1 - PSI(+1) * (R(+1) * OMEGA(+1))^(sigma - 1) * beta^sigma * pi/pi(+1);
//4
eps * pi = 1 - PSI(+1) * ((R(+1))^(sigma-1)*beta^sigma*gamma(+1))*(eps*pi)/(eps(+1)*pi(+1));
//5
OMEGA = omega + (1-omega)*eps^(1/(1-sigma));
//6
H = N * W + H(+1)/((1+n(+1))*(1+x(+1))*R(+1)*OMEGA(+1)/omega(+1));
//7
C * (1 + mu^sigma * (R(+1)*Pi(+1)/(R(+1)*Pi(+1)-1))^(sigma-1)) = pi * ((1 + (eps/gamma-1) * lambda(-1)) * R * A(-1) + H(-1));
//8
A = K + 1/R * M_d(+1)/P + Pf/P;
//9
PSI = (1 + ((R*Pi-1)/(R*Pi))^(sigma-1) * mu^sigma)^(-1) / (1 + (((R(+1)*Pi(+1))-1)/(R(+1)*Pi(+1)))^(sigma-1) * mu^sigma)^(-1);
// Firm side
//10
Y = (X * N)^alpha * (K)^(1-alpha)/price_disp;
//11
W = alpha * Y / N * mc;
//12
R = (1 - alpha) * Y / K * mc + 1 - delta;
//13
mc = (1/(1-alpha))^(1-alpha)*(1/alpha)^alpha*(W/X)^(1-alpha)*(R-(1-delta))^alpha;
// Calvo pricing
//14
PStar = epsilon/(epsilon-1) * g1/g2;
//15
g1 = P^epsilon * Y * mc + theta*beta * g1(+1);
//16
g2 = P^(epsilon-1) * Y + theta*beta * g2(+1);
//17
P = (theta * P(-1)^(1-epsilon) + (1-theta) * PStar^(1-epsilon))^(1/(1-epsilon));
//18
price_disp = theta*(PStar/P)^(-epsilon)*(P/P(-1))^(epsilon) + (1-theta)*(P/P(-1))^(epsilon)*price_disp(-1);
//19
Pi = P/P(-1);
// Dividends and share prices
Df = P * Y *(1-mc);
Pf(+1) + Df(+1) = R(+1) * Pf;
end;
//-----------------------------------------------------------------------//
//--------------------------- Initial Values ----------------------------//
//-----------------------------------------------------------------------//
initval;
M_d = 1; P =1;
x = 0.01;
n = 0.01;
omega = 0.97;
gamma = 0.9;
lambda =0.3878;
pi =0.2394;
eps =1.2832;
OMEGA =1.0124;
R =1.3968;
PSI =0.9988;
mc =0.9064;
Y =0.7407;
C =0.6857;
K =0.459;
H =1.4279;
A =1.365;
P =0.972;
PStar =0.9364;
W =0.448;
Pi =0.98;
Df =0.0681;
Pf =0.1732;
price_disp=1.0336;
g1 =0.601;
g2 =0.6877;
end;
model_diagnostics;
steady;
endval;
gamma = 0.94;
end;
steady;
simul(periods=300);
```
from http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=14206 does not run with
```
ERROR: the second-order cross partial of equation 14 w.r.t. trend variable X and endogenous variable PStar is not null.
```
but the relevant equation
```
PStar = epsilon/(epsilon-1) * g1/g2;
```
should have the trends specified (as far as I can see). `g1/g2=((X*N)^(1-epsilon))/((X*N)^(2-epsilon)=(XN)^(-1)`, which is the trend for `PStar`.4.6Sébastien VillemotSébastien Villemothttps://git.dynare.org/Dynare/dynare/issues/1377Decide on treatment of qz_criterium in estimation with particle filter2019-06-19T15:37:45ZJohannes Pfeifer Decide on treatment of qz_criterium in estimation with particle filterCurrently, if a unit root is present, estimation with `order=2` will result in an error. Using `diffuse_filter` will disable the check, but obviously makes no sense for particle filtering. See http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=13126Currently, if a unit root is present, estimation with `order=2` will result in an error. Using `diffuse_filter` will disable the check, but obviously makes no sense for particle filtering. See http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=131264.6https://git.dynare.org/Dynare/dynare/issues/1332Decide on how to deal with mh_recover on Octave2019-06-19T15:37:45ZJohannes Pfeifer Decide on how to deal with mh_recover on OctaveVarious unit test fail on Octave, because the `mh_recover` option does not work properly under Octave as there are differences in setting the random number generator. We can either
- disable the check in the unit test and accept that the behavior of `mh_recover` is different under Octave and Matlab (and then document this)
- or provide an error under Octave when someone tries to use this optionVarious unit test fail on Octave, because the `mh_recover` option does not work properly under Octave as there are differences in setting the random number generator. We can either
- disable the check in the unit test and accept that the behavior of `mh_recover` is different under Octave and Matlab (and then document this)
- or provide an error under Octave when someone tries to use this option4.5https://git.dynare.org/Dynare/dynare/issues/1063Make output of dsge_var_likelihood accessible2019-06-19T15:37:54ZJohannes Pfeifer Make output of dsge_var_likelihood accessibleSee the discussions in http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=7315 and http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=2920
See the discussions in http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=7315 and http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=2920
5.0https://git.dynare.org/Dynare/dynare/issues/670Fix handling of prefiltering and trends in non_linear_dsge_likelihood2019-06-19T15:38:08ZJohannes Pfeifer Fix handling of prefiltering and trends in non_linear_dsge_likelihood`non_linear_dsge_likelihood` uses
`Y = transpose(DynareDataset.rawdata);`
By accessing `rawdata` instead of data, prefiltering is ignored. Moreover, deterministic trends are not subtracted. I am not sure this is on purpose.
`non_linear_dsge_likelihood` uses
`Y = transpose(DynareDataset.rawdata);`
By accessing `rawdata` instead of data, prefiltering is ignored. Moreover, deterministic trends are not subtracted. I am not sure this is on purpose.
4.5https://git.dynare.org/Dynare/dynare/issues/667rename hessian.m in dyn_hessian.m2019-06-19T15:38:08ZMichelJuillardrename hessian.m in dyn_hessian.mIn order to avoid name collision with other Matlab program/toolboxe
In order to avoid name collision with other Matlab program/toolboxe
4.5https://git.dynare.org/Dynare/dynare/issues/573Fix bug in interaction of diffuse filter and stoch_simul2019-06-19T15:38:12ZJohannes Pfeifer Fix bug in interaction of diffuse filter and stoch_simulSee http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=5248
See http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=5248
4.5MichelJuillardMichelJuillardhttps://git.dynare.org/Dynare/dynare/issues/534Discuss allowed use of endogenous variables outside of model-block2019-06-19T15:38:14ZJohannes Pfeifer Discuss allowed use of endogenous variables outside of model-blockConsider the mod-file
```
var y, c, k, a, h, b;
varexo e, u;
parameters beta, rho, alpha, delta, theta, psi, tau test;
alpha = 0.36;
rho = 0.95;
tau = 0.025;
beta = 0.99;
delta = 0.025;
psi = 0;
theta = 2.95;
phi = 0.1;
test=y*beta;
model;
c*theta*h^(1+psi)=(1-alpha)*y;
k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1)))
*(exp(b(+1))*alpha*y(+1)+(1-delta)*k));
y = exp(a)*(k(-1)^alpha)*(h^(1-alpha));
k = exp(b)*(y-c)+(1-delta)*k(-1);
a = rho*a(-1)+tau*b(-1) + e;
b = tau*a(-1)+rho*b(-1) + u;
end;
initval;
y = 1.08068253095672;
c = 0.80359242014163;
h = 0.29175631001732;
k = 11.08360443260358;
a = 0;
b = 0;
e = 0;
u = 0;
end;
shocks;
var e; stderr 0.009;
var u; stderr 0.009;
var e, u = phi*0.009*0.009;
end;
stoch_simul;
```
The definition
`test=y*beta;`
results in the preprocessor plugging in for the endogenous variable `y` with its yet uncomputed steady state, i.e. 0. Users thus can create a circular problem with the steady state of `y` depending on `test` with `test` depending on `y` - and would never notice the problem, because the definition does not result in an error. Would it be possible to block this behavior? If we want to allow users to access the steady state value of endogenous variables outside of the model-block, it should be through the `steady_state`operator.
Or do I miss something that makes this behavior desirable?
Consider the mod-file
```
var y, c, k, a, h, b;
varexo e, u;
parameters beta, rho, alpha, delta, theta, psi, tau test;
alpha = 0.36;
rho = 0.95;
tau = 0.025;
beta = 0.99;
delta = 0.025;
psi = 0;
theta = 2.95;
phi = 0.1;
test=y*beta;
model;
c*theta*h^(1+psi)=(1-alpha)*y;
k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1)))
*(exp(b(+1))*alpha*y(+1)+(1-delta)*k));
y = exp(a)*(k(-1)^alpha)*(h^(1-alpha));
k = exp(b)*(y-c)+(1-delta)*k(-1);
a = rho*a(-1)+tau*b(-1) + e;
b = tau*a(-1)+rho*b(-1) + u;
end;
initval;
y = 1.08068253095672;
c = 0.80359242014163;
h = 0.29175631001732;
k = 11.08360443260358;
a = 0;
b = 0;
e = 0;
u = 0;
end;
shocks;
var e; stderr 0.009;
var u; stderr 0.009;
var e, u = phi*0.009*0.009;
end;
stoch_simul;
```
The definition
`test=y*beta;`
results in the preprocessor plugging in for the endogenous variable `y` with its yet uncomputed steady state, i.e. 0. Users thus can create a circular problem with the steady state of `y` depending on `test` with `test` depending on `y` - and would never notice the problem, because the definition does not result in an error. Would it be possible to block this behavior? If we want to allow users to access the steady state value of endogenous variables outside of the model-block, it should be through the `steady_state`operator.
Or do I miss something that makes this behavior desirable?
4.5https://git.dynare.org/Dynare/dynare/issues/504Discuss and document the load_mh_file option2019-06-19T15:38:16ZJohannes Pfeifer Discuss and document the load_mh_file optionThe current behavior of the `load_mh_file`-option with it recomputing the mode, the Hessian, and the scale-factor by default seems counterintuitive. Given the fixed seed, one should get the same results, but there is the risk of them changing between the reloaded chain and the new elements to be added and thus having a chain with difffering proposal densities. We should at least document this behavior and potentially reload the mode-file by default. See also http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=5051
The current behavior of the `load_mh_file`-option with it recomputing the mode, the Hessian, and the scale-factor by default seems counterintuitive. Given the fixed seed, one should get the same results, but there is the risk of them changing between the reloaded chain and the new elements to be added and thus having a chain with difffering proposal densities. We should at least document this behavior and potentially reload the mode-file by default. See also http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=5051
4.5https://git.dynare.org/Dynare/dynare/issues/338Clarify and potentially disentangle role played by conf_sig2016-08-21T14:14:28ZJohannes Pfeifer Clarify and potentially disentangle role played by conf_sigAccording to the manual, conf_sig by default is 0.9 for forecast, but after global_initialization, it is 0.6. conditional_forecast has an option with the same name and default 0.8 according to the manual.
According to the manual, conf_sig by default is 0.9 for forecast, but after global_initialization, it is 0.6. conditional_forecast has an option with the same name and default 0.8 according to the manual.
5.0https://git.dynare.org/Dynare/dynare/issues/332Add functionality to compute function on posterior2015-10-12T05:18:33ZJohannes Pfeifer Add functionality to compute function on posteriorCurrently, it is very hard to compute a user defined function on the posterior draws. A user asked me if we could add a functionality where the user specifies a function name which is executed on each posterior draw when we compute posterior moments. For such a function call, we would need to clearly specify the interface. We might want to provide M_, oo_, bayestopt_, estim_params_ and the current parameter draw as generic inputs (and nothing else). The tricky part consists of specifying the output arguments and making sure we can save them. One way would be to only accept one output argument and save it to the ii element of a cell array. By putting multiple outputs into a structure, the user could still effectively save multiple outputs while keeping a clearly specified interface for us.
Upon going over all draws, we would just save the cell array to the disk. It would be up to the user to make sense of the saved stuff.
Currently, it is very hard to compute a user defined function on the posterior draws. A user asked me if we could add a functionality where the user specifies a function name which is executed on each posterior draw when we compute posterior moments. For such a function call, we would need to clearly specify the interface. We might want to provide M_, oo_, bayestopt_, estim_params_ and the current parameter draw as generic inputs (and nothing else). The tricky part consists of specifying the output arguments and making sure we can save them. One way would be to only accept one output argument and save it to the ii element of a cell array. By putting multiple outputs into a structure, the user could still effectively save multiple outputs while keeping a clearly specified interface for us.
Upon going over all draws, we would just save the cell array to the disk. It would be up to the user to make sense of the saved stuff.
4.5https://git.dynare.org/Dynare/dynare/issues/314Should correlation field of oo_.PosteriorTheoreticalMoments be renamed to aut...2013-03-27T13:13:12ZSébastien VillemotShould correlation field of oo_.PosteriorTheoreticalMoments be renamed to autocorrelation?The current naming is confusing…
The current naming is confusing…
4.4https://git.dynare.org/Dynare/dynare/issues/1373Decide on whether evalute_smoother (and others) should set M_.params2019-06-19T15:37:45ZJohannes Pfeifer Decide on whether evalute_smoother (and others) should set M_.paramsThis picks up the discussion in https://github.com/DynareTeam/dynare/pull/1372#issuecomment-271336355
I agree with @MichelJuillard that changing `M_.params` in various functions is a bad idea as it makes it hard to trace what was going on in the mod-file. At the same time, I agree with @rattoma that keeping `M_.params` unchanged when calling `evaluate_smoother` would imply a break with previous versions and therefore mean a loss in backward compatibility. I could live with that, but usually @MichelJuillard prefers to be cautious in the regard. If we want to preserve backward compatibility, we need to make sure that `shock_decomposition` returns `M_` from `evaluate_smoother` to the base workspace, which was broken by https://github.com/DynareTeam/dynare/commit/2f717b5adc5a87f663c5c080f2963d1f65d1933eThis picks up the discussion in https://github.com/DynareTeam/dynare/pull/1372#issuecomment-271336355
I agree with @MichelJuillard that changing `M_.params` in various functions is a bad idea as it makes it hard to trace what was going on in the mod-file. At the same time, I agree with @rattoma that keeping `M_.params` unchanged when calling `evaluate_smoother` would imply a break with previous versions and therefore mean a loss in backward compatibility. I could live with that, but usually @MichelJuillard prefers to be cautious in the regard. If we want to preserve backward compatibility, we need to make sure that `shock_decomposition` returns `M_` from `evaluate_smoother` to the base workspace, which was broken by https://github.com/DynareTeam/dynare/commit/2f717b5adc5a87f663c5c080f2963d1f65d1933ehttps://git.dynare.org/Dynare/dynare/issues/1366Decide on how to deal with mistake in manual regarding updated variables2019-06-19T15:37:45ZJohannes Pfeifer Decide on how to deal with mistake in manual regarding updated variablesAccording to the manual, `smoother` triggers the computation of `oo_.UpdatedVariables`. But one actually needs `filtered_vars`. We can either
1. Correct the description in the manual to reflect the code behavior
2. Flag this as a bug as the code deviates from the manual and output `oo_.UpdatedVariables` when only the `smoother` option is specifiedAccording to the manual, `smoother` triggers the computation of `oo_.UpdatedVariables`. But one actually needs `filtered_vars`. We can either
1. Correct the description in the manual to reflect the code behavior
2. Flag this as a bug as the code deviates from the manual and output `oo_.UpdatedVariables` when only the `smoother` option is specifiedhttps://git.dynare.org/Dynare/dynare/issues/1296Decide on whether to save intermediate draws2019-06-19T15:37:47ZJohannes Pfeifer Decide on whether to save intermediate drawsWith the move to `posterior_sampling_core` we now by default save the MCMC draws every 50 draws into a temporary file, because we by default set
`posterior_sampler_options.save_tmp_file=1;`
I think this is very inefficient and therefore should be 0 by default (same behavior as before the move), with an interface provided to change the option.
@rattoma You added this behavior. Was the reason that `slice` should be treated differently?
With the move to `posterior_sampling_core` we now by default save the MCMC draws every 50 draws into a temporary file, because we by default set
`posterior_sampler_options.save_tmp_file=1;`
I think this is very inefficient and therefore should be 0 by default (same behavior as before the move), with an interface provided to change the option.
@rattoma You added this behavior. Was the reason that `slice` should be treated differently?
https://git.dynare.org/Dynare/dynare/issues/1252do we need to remove dynamic exception specifications?2019-08-14T12:13:30ZMichelJuillarddo we need to remove dynamic exception specifications?dynamic exceptions specification is now deprecated in C++
In mexFunction, when an unknown exception occurs, Matlab or Octave may crash
Maybe we should remove them from the mexFunction code. I don't see very well their contribution.
dynamic exceptions specification is now deprecated in C++
In mexFunction, when an unknown exception occurs, Matlab or Octave may crash
Maybe we should remove them from the mexFunction code. I don't see very well their contribution.
https://git.dynare.org/Dynare/dynare/issues/1229external functions and third order perturbation2019-06-19T15:37:49ZStéphane Adjemianstepan@dynare.orgexternal functions and third order perturbationIt appears that we did not implement the possibility to use external functions when solving DSGE models at third order. I thought that when the derivates are not provided by the user, Dynare computed the derivates numerically. It seems that this mechanism is not triggered at third order. Is this intentional?
It appears that we did not implement the possibility to use external functions when solving DSGE models at third order. I thought that when the derivates are not provided by the user, Dynare computed the derivates numerically. It seems that this mechanism is not triggered at third order. Is this intentional?
https://git.dynare.org/Dynare/dynare/issues/1210Allow string arrays to be passed to options2018-11-09T14:13:22ZHoutan BastaniAllow string arrays to be passed to optionsFollowing the discussion in #1199, allow the preprocessor to accept string array values: `['a' 'b' 'c']`
Following the discussion in #1199, allow the preprocessor to accept string array values: `['a' 'b' 'c']`
Houtan BastaniHoutan Bastanihttps://git.dynare.org/Dynare/dynare/issues/1024Save posterior moments even when moments are constant2019-06-19T15:37:56ZJohannes Pfeifer Save posterior moments even when moments are constantIn `covariance_mc_analysis.m` and the like we test whether the moments are constant and, if yes, we store NaN. I don't see the logic of this. All moments are still well-defined (although identical). I would propose to get rid of this check and always store the computed moments.
In `covariance_mc_analysis.m` and the like we test whether the moments are constant and, if yes, we store NaN. I don't see the logic of this. All moments are still well-defined (although identical). I would propose to get rid of this check and always store the computed moments.
https://git.dynare.org/Dynare/dynare/issues/1012Add one-sided HP filter2019-06-19T15:37:56ZJohannes Pfeifer Add one-sided HP filter#1011 already implemented the interface. Before adding the function, we need to decide whether to simply add it as a function that operates on regular double data like the `sample_hp_filter` or whether we want to make it a function on dseries objects as the `baxter_king_filter.m` of the dseries submodule.
#1011 already implemented the interface. Before adding the function, we need to decide whether to simply add it as a function that operates on regular double data like the `sample_hp_filter` or whether we want to make it a function on dseries objects as the `baxter_king_filter.m` of the dseries submodule.