About MCF Model User Datasets

Forecast Period versus Prediction Period
For the MCF model we will use the phrase "forecast period" to refer to the 2011-2020 period. If you make no changes to the MCF 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 MCF Model Datasets
The treatment of the MCF model datasets is similar to the treatment of the US model datasets. Everything about the MCF model is stored in a single dataset:

1. values on all the endogenous and exogenous variables from 1960:1 through 2020:4,
2. specification information, and
3. coefficient estimates.

The base dataset for the MCF model is called MCFBASE with a password of MCBASE. You will name and create your own dataset, using MCFBASE as a starting point. Say you call your dataset NEW and give it the password of NEW. When you first start, NEW and MCFBASE 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 MCFBASE 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 MCFBASE. 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 MCFBASE, 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 MCFBASE, which is 2011. 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 MCFBASE, 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 MCFBASE. 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 MCF 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 MCF model online 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 MCF model are annual, the MCF 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.

Solution Errors
If you make wild changes to the exogenous variables or coefficients, the model may not solve. Because the MCF model (unlike the US model) is not iterated until convergence---see the discussion in Chapter 2, Section 2.2, in The MCF Model Workbook: January 29, 2011 (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 2005 US dollars. The are 58 countries in the model plus an "all other" category, 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 site.

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.