For the MCJ model the data are available for the period 1960:1--2016:4,
although for some variables and countries the data begin later. A value of
-99.0 is used for missing observations.
You can examine the data
(list, graph, download, etc.)
beginning with 1960.1. When solving the model, you should not begin before
1972. From 1972 on the data for most countries exist.
The MCJ Model Datasets
The treatment of the MCJ model datasets is similar to the treatment of
the US model datasets. Everything about the MCJ 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 MCJ model is called MCJBASE with a
password of MCBASE. You will name and create your own
dataset, using MCJBASE as a starting point. Say you call your dataset NEW
and give it the password of NEW. When you first start, NEW and MCJBASE
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 MCJBASE 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 MCJBASE. 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 JAY in MCJBASE, 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: 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 MCJBASE, 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 MCJBASE. 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 MCJ Model are at quarterly rates, and you
should keep this in mind in working with the model.
Years versus Quarters
Since some of the individual country models within the overall MCJ model
are annual, the MCJ 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 MCJ model (unlike the US model) is not iterated until
convergence---see the discussion in Chapter 1, Section 1.2, in
The MCJ Model Workbook: 2018---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.
Dataset Names and Storage
The dataset names cannot be longer than EIGHT (8) 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.