Dynare forums

Hello dear all,
I am interested in get conditionals forecast after estimation, my model has five exogenous variables and I would like to know if there are some recomendation to choose

controlled_varexo

What exogenous variables could I include in controlled_varexo?

and I would like to know the research paper in which it is based in order to understand better the methodology.


Thanks a lot

Aldo

Usually the idea is that you can control these shocks, i.e. they are policy instruments. Say you want to keep GDP at 1 percent above trend. What is the sequence of monetary policy shocks, i.e. deviations from the standard monetary policy rule, that are necessary to achieve this.

I am not aware of a reference. You can find a description of what is going on at http://www.dynare.org/phpBB3/viewtopic.php?f=1&t=5974

Thanks dear jpfeifer,

I would like to get conditional forecast using RAMSES II (Christiano, Trabandt and Walentin (2011)).
Variables I want to give constrained path are GDP, foreign GDP, interest rate, foreign interest rate, exchange rate among some others.

I understand that conditional forecast of dynare needs to especify shocks we can control to achieve the constrained path, but I am more interested in checking what happens with unconditioned variables when GDP, interest rate and exchange rate follow the constrained path ("scenario analysis"). Is there some way to do this with dynare?

I appreciate very much your help.

Thanks

Aldo

Then pick as many shocks for controlling as controlled variables. Note that these variables must be controllable. For example, if foreign output follows an exogenous process with only one shock, you cannot use monetary policy shocks in a small open economy to controll foreign output.

Thanks dear jpfeifer,

Given is not very clear to me how to choose shocks for controlling controlled variables, I am thinking use the methodology of Banbura, Gianonne and Lenza: "CONDITIONAL FORECASTS AND SCENARIO ANALYSIS WITH VECTOR AUTOREGRESSIONS FOR LARGE CROSS-SECTIONS".

https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1733.pdf?34d83c4b9cc14af18001108a69abb1c3

They don't need to specify these shocks, and it can also be applied to a state space representation like a DSGE model, in short they start from the idea (details is found in pag. 16)

In fact, the variables for which we do not assume the knowledge of a future path can be considered as time series with missing data. The Kalman filter allows to easily deal with such time series

and their algorithm follows these steps:

1. They derive a state space representation from the original s-s by removing the rows, also columns that correspond to the missing observations.
2. Draw states using smoother simulation for the modified (for the missing data) state space representation.
3. Using measure equation compute future values of unconditioned variables.

In this respect I found this reply in Help:

Re: Missing observations
Postby jpfeifer » Sun Nov 04, 2012 9:25 am

Dynare will use the longer sample and treat the missing values as unobserved states during the Kalman filter routine, i.e. their best forecast is inferred from the model and the remaining data.

And my question are:

1. If I could consider unconditioned variables as missing observations ? so when they are estimated in Dynare, may I consider them as forecasts?
2. Related to first. Is there only point estimated for missing observations? or Can we find output from several paths in order to compute percentiles?

Thanks,

Aldo

Sorry, but I don't understand your questions. But what Banbura et al describe should be doable in Dynare. If I understand it correctly (I haven't read the full paper), you first estimation so that you have the parameters estimated on that data set. After that, run the calibrated smoother on the extended dataset that contains the controlled values for the variables and NaN for the non-controlled variables. If you want to get a distribution based on the posterior distribution of parameters, you may need to use the

Code: Select all

posterior_function

and within it call

Code: Select all

evaluate_smoother

manually.
Regarding percentiles: the Kalman smoother will return the mean and variance for the objects of interest. Given the normal distribution underlying this, you can compute any quantile.