Forecast Period versus Prediction Period
For the MCC model we will use the phrase "forecast period" to
refer to the 2007-2009 period. If you make no changes to the MCC model and
solve it for this period, the predicted values will be
the values that are in the base dataset. You may, however, deal with other
periods. Much of the data go back to 1960, and you can examine the data
(list, graph, download, etc.)
beginning with 1960. When solving the model, you should not begin before
1976, since data for a number of countries do not begin until about 1976.
The period that you choose to work with will be called the
"prediction period," which may, of course, differ
from what we are calling the forecast period.
The MCC Model Datasets
The treatment of the MCC model datasets is similar to the treatment of
the US model datasets. Everything about the MCC model is stored in a single
1. values on all the endogenous and exogenous variables from 1960:1
2. specification information, and
3. coefficient estimates.
The base dataset for the MCC model is called MCCBASE with a
password of MCBASE. You will name and create your own
dataset, using MCCBASE as a starting point. Say you call your dataset NEW
and give it the password of NEW. When you first start, NEW and MCCBASE
are exactly the same, and then NEW gets modified as you make
changes (change exogenous variables, coefficients, etc.). Once you are
done making changes, you tell the program to solve the model. The program
solves the model, and after this solution the values of the endogenous
variables in NEW are the predicted values. You can examine (i.e., write to
the screen, print to a printer, or download to a file) the
values in MCCBASE and in any datasets you create.
In many cases one is interested in how the values of the endogenous
variables in NEW compare to the original values in MCCBASE. Say that
you changed German government purchases of goods (GEG) for the forecast
period and are interested in how this change
affected real Japanese GDP (JAY). You can simply tell the program that
you want to compare JAY in NEW versus MCCBASE, and it will show you the
two sets of values and the differences.
(Once you do this a few times you will get the hang of it.)
WARNING: A comparison done
this way only works if you take the beginning of your prediction period to
be no earlier than the beginning of the forecast period in MCCBASE, which
is 2007. Otherwise, you should set the errors equal to the historical
errors before solving. The other option, which is not as convenient,
is to use the procedure discussed in
Section 2.6 in Chapter 2 of
The US Model Workbook.
The program is flexible as to what you take as your base dataset.
Although the first time you start the base dataset is MCCBASE, after
you have created new ones, you can use any of these as your base.
For example, if you created NEW and now want to makes
further changes and create NEW1, you simply tell the program that you want
NEW as your base dataset. Once you have created NEW1, you can either
compare the values in NEW1 with those in NEW or the values in NEW1 with
those in MCCBASE. You do this by simply telling the
program which two datasets to use for the comparison.
If you make changes to your dataset and do not ask the program to
solve it (which you are allowed to do), the values of the endogenous
variables in the dataset are not consistent with your changes because
they are not the solution values. Make sure you
remember whether your dataset has been solved. As a general rule, you
might always solve
your datasets immediately after you have made your changes.
Units of the Variables
The flow variables in the MCC Model are at quarterly rates, and you
should keep this in mind in working with the model. Contrary to the
case for the US model alone online, the flow variables for the MCC
are always presented at quarterly rates. (For the US model alone, the
flow variables are presented at annual rates.)
Years versus Quarters
Since some of the individual country models within the overall MCC model
are annual, the MCC model must be solved in yearly (four-quarter) units.
Thus, when choosing a period, you need only (and can only) choose years.
The solution is, however, quarterly for the quarterly countries, and when
you examine the output, you will be allowed to examine
quarterly values for the quarterly countries.
If you make wild changes to the exogenous variables or coefficients,
the model may not solve.
Because the MCC model (unlike the US model) is not iterated until
convergence---see the discussion in Chapter 2, Section 2.2, in
The MCC Model Workbook: August 1,
2006 (pdf)---you will not get an
error message if the model does not solve.
If the "solution" values do not look sensible and you are worried that
the model has not solved, you can increase LIMITA and LIMITB and see if you
get the same results. If the results are noticeable different, then the
model has not solved. You need to be less wild with your changes
and try again. As a general rule, as discussed
in Chapter 2 of
The US Model Workbook,
you should not try to push the economy into extreme areas. Macroeconometric
models are not likely to be reliable when variable values are pushed far
beyond their historical ranges.
Space for Trade Flow Variables
When you create your dataset you will be asked if you want to allow space
for the trade share output. The trade share output consists of data on
the trade flow variables XX00$ij in the model. For a given i,j pair,
XX00$ij is the level of exports from country i to country j in 2000 US
dollars. The are 58 countries in the model plus an "all other"
and so the trade flow variables take up considerable space. If you are
not going to examine the output for any of these variables, do not
allow space for them. This does not change the solution of the model at
all (the trade flow variables are still used in the solution of the model);
it just means that the output for these variables is not written to
the dataset. Your job will be slightly faster if you do not allow space
since there is less writing to disk, and it saves on disk space for the
Dataset Names and Storage
The dataset names cannot be longer than EIGHT characters. A common mistake
is to use more than eight characters. The datasets are stored on the
site's hard drive for about three months. If you want to continue to use
a dataset older than three months, simply copy it to a new named dataset,
and the latter will be treated as a newly created dataset.