Technology has opened new frontiers. Supercomputers a thousand times more powerful than yesterday's mainframes now exist. The availability of raw computing power no longer is a constraint as it was in early modelling efforts. The next generation of 32 bit desk-top
microcomputers will be able to run the largest of today's models. Supermodels 10 to 20 times larger can be handled by mainframes.
Important fundamental constraints on macroeconomic modelling still exist. The greatest
constraint of all is probably the inability of economists to capture mathematically in models the workings of hugely complex modern economies. Nevertheless, great strides have been
accomplished. As economic theory develops and is better applied, and as the quality and quantity of data on the economy improves, further progress will be made in macroeconomic modelling.
A less fundamental constraint is our ability to understand and manage a model. In any modelling project there must be at least one individual who fully understands all of the model's parts and how they fit together. The larger and more complex the model, the more difficult the task.
The human brain becomes the ultimate constraint on the size and complexity of a model.
A dissatisfaction with the way models have been put together and the lack of understanding of long-run properties have led researchers at the Bank of Canada to put together SAM. The small size of the model has enabled its builders to utilize much more complicated specifications and have a greater degree of simultaneity. The imposition of a priori constraints and the
interrelated nature of many sectors have necessitated the use of more sophisticated estimation techniques.
The MACE model is also small, but was not made small to allow its builders to increase its complexity. Instead its builders believed that existing models were getting too large and fragmented and wanted to develop a model that was small enough to guarantee both timeliness and economic coherence. The MACE model reflects this approach. It is a small model with a simple yet theoretically rigourous structure suitable for general macroeconomic analysis.
Modularity is another way of relaxing the human constraint. If sectors or sub-models can be segmented in reasonable ways and their interactions can be clearly specified, then it is possible to build models in a modular fashion. This enables larger models to be built.
Satellite models offer a closely related approach that holds some promise of overcoming the comprehensibility and complexity problem. Core models can be kept as simple as possible.
Satellite models can be plugged in replacing a particular sector or sectors for specific applications, provided the appropriate interfaces with the core model exist.
For many of the most interesting applications, the interfaces will have to allow for two-way feedbacks. This renders the development of general core models more difficult.
The development of increasingly sophisticated software for model-building and management will help to relax the human constraint. TROLL, TSP and other advanced scientific programming packages are not the last word in econometric and simulation software and improvements can be made that will lessen the burden of updating data bases, estimating equations, assembling
equations into sectors, simulating sectors and the complete model, analyzing the model's structure and dynamics, performing tracking simulations, and preparing documentation. These are the mundane tasks that occupy most of the model-builder's time. If they become less
burdensome, the model builders will both have more time and be better informed in making the key choices that are at the heart of model-building.
One particular futuristic possibility that was raised in discussions with modelling groups was the use of artificial intelligence front-ends to facilitate the building, management and use of large models.
The the development of software for model-building and management since RDX2 has been in many respects remarkable. The old RDX2 simulator and estimation and database packages were relatively cumbersome in relation to the new generation of interactive programs such as TROLL and TSP.
The degree of progress in the development of the models themselves has been somewhat less impressive. In such areas as the supply side and interrelated factor demands the present quarterly models actually represent a step backwards. In other areas models have continued to develop in line with the new theory and events. Examples include the adoption of the extended Phillips curve as the accepted wage specification, the recognition of the productivity slowdown, the incorporation of energy, and the modification of financial sectors to reflect monetary targeting.
Quarterly models have also introduced industrial disaggregation. This has been done by private sector forecasting agencies to meet the demands of their clients.
It is very difficult to foresee the future course of Canadian macroeconomic modelling. It will of course be influenced by developments in theory, data, events and technology, not necessarily in that order. Efforts will be made to remedy obvious deficiencies in existing models.
For the large annual models based on input/output the availability of input/output time series opens up new possibilities. For small annual models, the spread of microcomputers is likely to make them more widely available. The FAIR model in the U.S. is already on the market. A group
at Laval has mounted MACE on a microcomputer. The Bank is considering the possibility of providing SAM on a floppy disk. This will give many more people, particularly students and professors in universities, an opportunity to become familiar with macro-models and how they work. It could help to train a new generation of modellers. It may spawn interest in developing satellite or special purpose models.
This too can be done on a microcomputer.
There are an number of promising areas for future research that can be identified. The
specification of the supply side of macro-models and its consistent integration into the overall model framework should remain a priority for model-builders. The task will be particularly difficult for the models with industrial disaggregation.
The treatment and role of expectations is another area requiring future research. This would be much aided if some attention were to be paid to the development of price and output expectation series. Surveys offer one promising approach for obtaining data on expectations. However, it will be quite a while before consistent time series are available and it will be even longer if the
process does not start soon.
In keeping with the research on the supply side and expectations, it will be important to focus much more on the overall dynamic properties of macro-models and ensuring consistency among sectors. This will require a rigorous program of complete and partial simulation analysis.
Various recent developments will no doubt be fruitful topics for model related research. The sharp and substantial rise in the level of real interest rates since 1980 has been out of the
bounds of earlier historical experience and has no doubt had widespread impact on the economy.
This offers an opportunity for researchers to learn more about the links between the financial and real sectors.
The high and volatile savings rate is another issue that should be explored. Important questions are the extent to which it has been influenced by inflation and by nominal and real interest rates. Also it is important to understand the significance of various institutional factors. A two way attack on the savings rate would appear to be most promising - directly as in CANDIDE 2.0 and indirectly through the estimation of disaggregated consumption functions. In the estimation of consumption functions it would be useful to explore the role of wealth. This is a variable that is
very important in theory, but that has not been much utilized in larger macro-models because of the difficulty of obtaining high quality and timely data.
Another important issue that can be addressed through macro-models is crowding-out. The influence of deficits and debt on asset demand, interest rates and the exchange rate can be analyzed empirically in macroeconomic models. It would also be useful to examine the possible international transmission of crowding-out from the United States.
At a lower level of generality, the housing sector of most models is generally unsatisfactory.
There might be some payoff to attempting a more detailed modelling of the various components of the housing market and of the behaviour of the main decisionmakers involved.
APPENDIX 1