dynare issueshttps://git.dynare.org/Dynare/dynare/-/issues2019-11-21T08:36:41Zhttps://git.dynare.org/Dynare/dynare/-/issues/835Behavior of STEADY_STATE() in perfect foresight models with anticipated perma...2019-11-21T08:36:41ZStéphane Adjemianstepan@adjemian.euBehavior of STEADY_STATE() in perfect foresight models with anticipated permanent shocks.In the current state, `STEADY_STATE(X)` return the terminal steady state of variable X in the dynamic model (`oo_.steady_state(i)`). Is this correct in the periods preceding the permanent shock? What if we have more than one permanent shock (at different periods)? @FerhatMihoubi raised this issue yesterday in a discussion. There is a mechanism in the bytecode routines to handle this case by considering different steady state between each (expected) permanent shock (actually this part of the code is not working). For me it is far from obvious that the steady state should change (unless the permanent shocks are unexpected, but there is noisy interface for this kind of scenario in Dynare).
In the current state, `STEADY_STATE(X)` return the terminal steady state of variable X in the dynamic model (`oo_.steady_state(i)`). Is this correct in the periods preceding the permanent shock? What if we have more than one permanent shock (at different periods)? @FerhatMihoubi raised this issue yesterday in a discussion. There is a mechanism in the bytecode routines to handle this case by considering different steady state between each (expected) permanent shock (actually this part of the code is not working). For me it is far from obvious that the steady state should change (unless the permanent shocks are unexpected, but there is noisy interface for this kind of scenario in Dynare).
https://git.dynare.org/Dynare/dynare/-/issues/827Feature request: Implement the algorithm of Ajevskis (2014) for perturbation ...2019-11-21T08:36:41ZTom HoldenFeature request: Implement the algorithm of Ajevskis (2014) for perturbation around a deterministic pathViktors Ajevskis has a [great new paper](https://ideas.repec.org/p/ltv/wpaper/201401.html) showing how the Lombardo pruning method may be extended to support perturbation around a deterministic path. This seems like a feature that would be a huge benefit to virtually all Dynare users, since it makes it possible to e.g.:
- Solve non-stationary DSGE models, such as those featuring structural change.
- Evaluate the welfare consequences of a permanent change in policy, without sacrificing accuracy along the transition path.
- Obtain increased accuracy for large impulse response exercises.
- Unify the stochastic and non-stochastic simulation engines in Dynare.
It would be great if this could be added to the bottom of your very long to do list!
https://ideas.repec.org/p/ltv/wpaper/201401.html
Viktors Ajevskis has a [great new paper](https://ideas.repec.org/p/ltv/wpaper/201401.html) showing how the Lombardo pruning method may be extended to support perturbation around a deterministic path. This seems like a feature that would be a huge benefit to virtually all Dynare users, since it makes it possible to e.g.:
- Solve non-stationary DSGE models, such as those featuring structural change.
- Evaluate the welfare consequences of a permanent change in policy, without sacrificing accuracy along the transition path.
- Obtain increased accuracy for large impulse response exercises.
- Unify the stochastic and non-stochastic simulation engines in Dynare.
It would be great if this could be added to the bottom of your very long to do list!
https://ideas.repec.org/p/ltv/wpaper/201401.html
https://git.dynare.org/Dynare/dynare/-/issues/568Integrate DMM2019-11-21T08:36:41ZHoutan BastaniIntegrate DMMhttp://ipsc.jrc.ec.europa.eu/?id=790
http://ipsc.jrc.ec.europa.eu/fileadmin/repository/sfa/finepro/software/DMMmanual.pdf
http://ipsc.jrc.ec.europa.eu/?id=790
http://ipsc.jrc.ec.europa.eu/fileadmin/repository/sfa/finepro/software/DMMmanual.pdf
MichelJuillardMichelJuillardhttps://git.dynare.org/Dynare/dynare/-/issues/1162Handling of trends2019-11-21T08:36:41ZMichelJuillardHandling of trendsCurrently trends are taken into account only if users indicate them for observed variables. However, in random walk with drift, a deterministic trend appears endogenously for variables that are not necessarily observed.
Let consider the following model where only Y is observed:
```
A_t = A_{t-1} + g + e_t
Y_t = A_t + eta_t
```
Both A_t and Y_t contain a deterministic linear trend with a slope of g. The current practice of only specifying the trend of Y is not satisfactory anymore.
When using the smoother, we need to recognize these trends and include them in SmoothedVariables and friends
Currently trends are taken into account only if users indicate them for observed variables. However, in random walk with drift, a deterministic trend appears endogenously for variables that are not necessarily observed.
Let consider the following model where only Y is observed:
```
A_t = A_{t-1} + g + e_t
Y_t = A_t + eta_t
```
Both A_t and Y_t contain a deterministic linear trend with a slope of g. The current practice of only specifying the trend of Y is not satisfactory anymore.
When using the smoother, we need to recognize these trends and include them in SmoothedVariables and friends
https://git.dynare.org/Dynare/dynare/-/issues/1160Implement filter_covariance option in Bayesian estimation2019-11-21T08:36:41ZJohannes Pfeifer Implement filter_covariance option in Bayesian estimationCurrently only possible in ML and calibrated smoother
Currently only possible in ML and calibrated smoother
https://git.dynare.org/Dynare/dynare/-/issues/1081Add Chandrasekhar Recursion Kalman Filter2019-11-21T08:36:41ZJohannes Pfeifer Add Chandrasekhar Recursion Kalman FilterAs discussed during the 2015 Dynare conference, this might be a nice addition for larger models. See
Herbst (2015): "Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models", Comput Econ (2015) 45:693–705
As discussed during the 2015 Dynare conference, this might be a nice addition for larger models. See
Herbst (2015): "Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models", Comput Econ (2015) 45:693–705
https://git.dynare.org/Dynare/dynare/-/issues/1078Make lyapunov_symm compatible with sparse matrices2019-11-21T08:36:41ZJohannes Pfeifer Make lyapunov_symm compatible with sparse matricesAs discussed in #988 it would make sense to have `lyapunov_symm` able to work with sparse matrices (at least for the baseline option)
As discussed in #988 it would make sense to have `lyapunov_symm` able to work with sparse matrices (at least for the baseline option)
https://git.dynare.org/Dynare/dynare/-/issues/237Document sbvar command2019-11-21T08:36:41ZSébastien VillemotDocument sbvar commandMichelJuillardMichelJuillardhttps://git.dynare.org/Dynare/dynare/-/issues/762svar identification2019-11-21T08:36:41ZHoutan Bastanisvar identificationimplement partial/global identification for svar, based on Tao's paper
implement partial/global identification for svar, based on Tao's paper
MichelJuillardMichelJuillardhttps://git.dynare.org/Dynare/dynare/-/issues/1154Investigate crash of Octave under Windows2019-11-21T08:36:40ZJohannes Pfeifer Investigate crash of Octave under WindowsThere are two problems with the following mod-file under Octave
```
var ygap pinf i u p a r_nat;
varexo eu ea;
parameters csigma cbeta crhou ckappa cweightygap crpinf cry clambda cepsilon ctheta cphi crhoa calfa;
csigma = 1.0;
cbeta = 0.99;
crhou = 0.8 ;
cweightygap = .1;
crpinf = 1.5;
cry = 0.125;
cepsilon = 6;
ctheta = 2/3;
calfa = 0.33;
cphi = 1;
clambda = (1-ctheta)*(1-ctheta*cbeta)/ctheta *(1-calfa)/(1-calfa+calfa*cepsilon);
ckappa = clambda*(csigma+(cphi+calfa)/(1-calfa));
calphax = ckappa/cepsilon;
crhoa =0.9;
model(linear);
ygap = ygap(+1) - (1/csigma)*(i-pinf(+1) - r_nat);
pinf = cbeta*pinf(+1) + ckappa*ygap + u;
u = crhou*u(-1) + eu;
p=p(-1)+pinf;
// the exogenous processes for productivity
a = crhoa*a(-1) + ea;
// the process for the natural rate can be derived from the flexible economy solution
r_nat = csigma*(1+cphi)/((1-calfa)*csigma+cphi+calfa)*(crhoa-1)*a;
end;
shocks;
var eu; stderr 1;
var ea; stderr 1;
end;
// ramsey
planner_objective(ckappa/cepsilon*ygap^2+pinf^2);
ramsey_policy(planner_discount=0.99,order=1,instruments=(i),irf=12);
```
First of all, when no mex-files are present, `mjdgges.m` returns very different results than Matlab or the mex-file. While Octave then returns a rank failure, Matlab finds a small, but OK condition number and continues.
Second, when using the mex-files, Dynare crashes Octave 3.8.2 when computing theoretical moments. The culprit is the call to `ordschur.oct` in `lyap_symm`. The attached files allow to reproduce the error:
[crash_octave.zip](https://github.com/DynareTeam/dynare/files/193860/crash_octave.zip)
My guess is that the problem stems from `p`, which has a unit root and therefore non-finite second moments. This seems to be incorrectly handled.
There are two problems with the following mod-file under Octave
```
var ygap pinf i u p a r_nat;
varexo eu ea;
parameters csigma cbeta crhou ckappa cweightygap crpinf cry clambda cepsilon ctheta cphi crhoa calfa;
csigma = 1.0;
cbeta = 0.99;
crhou = 0.8 ;
cweightygap = .1;
crpinf = 1.5;
cry = 0.125;
cepsilon = 6;
ctheta = 2/3;
calfa = 0.33;
cphi = 1;
clambda = (1-ctheta)*(1-ctheta*cbeta)/ctheta *(1-calfa)/(1-calfa+calfa*cepsilon);
ckappa = clambda*(csigma+(cphi+calfa)/(1-calfa));
calphax = ckappa/cepsilon;
crhoa =0.9;
model(linear);
ygap = ygap(+1) - (1/csigma)*(i-pinf(+1) - r_nat);
pinf = cbeta*pinf(+1) + ckappa*ygap + u;
u = crhou*u(-1) + eu;
p=p(-1)+pinf;
// the exogenous processes for productivity
a = crhoa*a(-1) + ea;
// the process for the natural rate can be derived from the flexible economy solution
r_nat = csigma*(1+cphi)/((1-calfa)*csigma+cphi+calfa)*(crhoa-1)*a;
end;
shocks;
var eu; stderr 1;
var ea; stderr 1;
end;
// ramsey
planner_objective(ckappa/cepsilon*ygap^2+pinf^2);
ramsey_policy(planner_discount=0.99,order=1,instruments=(i),irf=12);
```
First of all, when no mex-files are present, `mjdgges.m` returns very different results than Matlab or the mex-file. While Octave then returns a rank failure, Matlab finds a small, but OK condition number and continues.
Second, when using the mex-files, Dynare crashes Octave 3.8.2 when computing theoretical moments. The culprit is the call to `ordschur.oct` in `lyap_symm`. The attached files allow to reproduce the error:
[crash_octave.zip](https://github.com/DynareTeam/dynare/files/193860/crash_octave.zip)
My guess is that the problem stems from `p`, which has a unit root and therefore non-finite second moments. This seems to be incorrectly handled.
https://git.dynare.org/Dynare/dynare/-/issues/147Accuracy checks2019-11-21T08:36:40ZSébastien VillemotAccuracy checkshttps://git.dynare.org/Dynare/dynare/-/issues/1208updated2histval2019-11-21T08:36:40ZMarco Rattoupdated2histvalfor real time forecasting exercises, it would be useful a utility
`updated2histval`
with the same behavior as:
`smoother2histval`
but that uses `oo_.UpdatedVariables` in place of `oo_.SmoothedVariables`
would this be feasible?
for real time forecasting exercises, it would be useful a utility
`updated2histval`
with the same behavior as:
`smoother2histval`
but that uses `oo_.UpdatedVariables` in place of `oo_.SmoothedVariables`
would this be feasible?
https://git.dynare.org/Dynare/dynare/-/issues/1338risky steady state: no interface, test suite2019-11-21T08:25:49ZHoutan Bastanirisky steady state: no interface, test suiteThere are several issues with risky steady state:
1. It has no interface. Why? Is this something we want to keep (hidden) in Dynare?
1. It the .mod files that use it are not in the test suite. Why?
There are several issues with risky steady state:
1. It has no interface. Why? Is this something we want to keep (hidden) in Dynare?
1. It the .mod files that use it are not in the test suite. Why?
4.7Sébastien VillemotSébastien Villemothttps://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/1664Implement option to use LMMCP for steady state computation2019-11-09T17:37:42ZMichelJuillardImplement option to use LMMCP for steady state computationIntroducing mixed complementarity problems in steady state computation may be useful to exclude parts of the definition space where no solution exists. It may also help when one doesn't know whether an occasionally binding constraints bites at the steady state or not, depending on the value of the parameters.Introducing mixed complementarity problems in steady state computation may be useful to exclude parts of the definition space where no solution exists. It may also help when one doesn't know whether an occasionally binding constraints bites at the steady state or not, depending on the value of the parameters.https://git.dynare.org/Dynare/dynare/-/issues/1478Configure Windows Installer to allow silent install2019-10-23T14:40:47ZJohannes Pfeifer Configure Windows Installer to allow silent installThis is a feature request from a university wanting to do an unattended installation of Dynare on a computer pool. The NSIS installer we use seems to not be configured for silent installs, i.e. passing '/D /d=test_directory' does not work.
We should follow the instructions at http://nsis.sourceforge.net/Docs/Chapter4.html#silent t to allow for this.
This is a feature request from a university wanting to do an unattended installation of Dynare on a computer pool. The NSIS installer we use seems to not be configured for silent installs, i.e. passing '/D /d=test_directory' does not work.
We should follow the instructions at http://nsis.sourceforge.net/Docs/Chapter4.html#silent t to allow for this.
Sébastien VillemotSébastien Villemothttps://git.dynare.org/Dynare/dynare/-/issues/1628Document new preprocessor options2019-10-09T10:33:13ZJohannes Pfeifer Document new preprocessor optionsAs far as I can see, the options `[output=dynamic|first|second|third]` and `[language=julia]` have not yet been documentedAs far as I can see, the options `[output=dynamic|first|second|third]` and `[language=julia]` have not yet been documented4.6https://git.dynare.org/Dynare/dynare/-/issues/1661`fast` option to dynare does not recompile every time the model changes2019-10-08T07:17:30ZHoutan Bastani`fast` option to dynare does not recompile every time the model changesThe temporary terms of the equations are not printed in the buffer on which the checksum is calculated. Hence, if the model is changed in a term that then becomes a temporary term, the checksum does not change.The temporary terms of the equations are not printed in the buffer on which the checksum is calculated. Hence, if the model is changed in a term that then becomes a temporary term, the checksum does not change.4.6Houtan BastaniHoutan Bastanihttps://git.dynare.org/Dynare/dynare/-/issues/1575Fix WriteShockDecomp2Excel.m2019-09-24T11:16:39ZJohannes Pfeifer Fix WriteShockDecomp2Excel.mSee https://forum.dynare.org/t/bug-in-write-xls-option-of-shock-decomposition-after-estimation/11097
On MAC, we rely on `xlwrite` from https://fr.mathworks.com/matlabcentral/fileexchange/38591-xlwrite--generate-xls-x--files-without-excel-on-mac-linux-win without checking whether Excel is installed. If we want to keep this, we need to clearly spell out that users need to install that file. @rattoma What do you think?See https://forum.dynare.org/t/bug-in-write-xls-option-of-shock-decomposition-after-estimation/11097
On MAC, we rely on `xlwrite` from https://fr.mathworks.com/matlabcentral/fileexchange/38591-xlwrite--generate-xls-x--files-without-excel-on-mac-linux-win without checking whether Excel is installed. If we want to keep this, we need to clearly spell out that users need to install that file. @rattoma What do you think?4.6https://git.dynare.org/Dynare/dynare/-/issues/1658Write a howto on forecasting2019-09-20T13:06:55ZSébastien VillemotWrite a howto on forecasting