side of these theoretical strengths was that sam was unsuitable for short - term projections. so sam was used to complement rdxf. the longer - term plan was to use sam as a prototype to build a quarterly model with the same key properties to replace rdxf. thus, qpm — for quarterly projection model — was built. it incorporated forward - looking consumers and companies with rational expectations and a coherent steady state. its short - run dynamics fit the data well enough to be used for projections. it also embraced all of the stock - flow dynamics necessary to analyze the significance of rising government debt, a prominent issue in the early 1990s. qpm represented a big leap in sophistication, and the desktop computers we had at the time were not up to the task. as the chief of the research department, i had to make a special request of then - governor gordon thiessen for money to buy more powerful computers just to run the experimental model — a process that nevertheless lasted all night. qpm served the bank well for more than a decade. its main shortcoming was that it could not deal with shocks to canada ’ s terms of trade — essentially, fluctuations in the prices of key commodities, such as oil — and these would become larger and more frequent. so adding a commodity sector to qpm moved to the top of our project list. at the same time, the economics literature was shifting to a new class of models : dsge, for dynamic stochastic general equilibrium. dsge models capture the idea that economic behaviours — such as decisions about household consumption and business investment — are perfectly informed, forward - looking and always optimal. these models also predict how an economy can evolve over time as expectations change. furthermore, stochastic shocks are built into the model at the household and firm level, dealing completely with the lucas critique. the model ’ s solution describes an economy that has reached a state of general equilibrium, with individual decisions aggregated into economy - wide prices and production quantities. so the bank decided to make the major investment to build totem — the terms - of - trade economic model. totem kept all the functionality of qpm, while adding the commodity sector and using the dsge paradigm. again, this work proved to be too much for the standard workstations that staff had on their desks, despite their increased power. calibrating totem required an extremely complex series of mathematical problems that took up enormous amounts of computing power. the

##comings. and to complement them, we have developed a number of “ satellite models ” to deal with specific issues. for example, the bank has built a model called mp2 — the macroprudential and monetary policy model — to study the impact of financial shocks and macroprudential policies on the economy. other models look at the ways inefficiencies in financial markets can lead to financial imbalances. there have also been ambitious efforts to model exports, at a very granular level, in light of extensive destruction of export capacity over the past decade. this multi - model strategy has allowed us to successfully mitigate the limitations of the current generation of models and appropriately manage the risks facing monetary policy. but the next generation of models at central banks will need to address these issues directly. the next generation of models while no one can say with any certainty what the next generation of central bank models will look like, we can expect them to stand on the shoulders of models like qpm, totem and lens. one lesson we have learned over the years is that a single model is unlikely to satisfy all our needs. this is a consequence of the fact that models are, by construction, an abstraction from reality. striking the right balance between theory and data fit is more an exercise in judgment than an empirical one, and those judgments are best formed by drawing upon complementary models. another lesson is that central banks have traditionally stuck with their models until well after their “ best before date. ” this is no doubt because greater realism in models means greater complexity, more computing power and big investments in research and people. guarding against keeping a model too long may mean continuously investing in new approaches, even when there are no obvious shortcomings in existing models. indeed, often it is the unforeseen advances in modelling paradigms — enabled by improved computer technology — that drive modelling progress. today, the dsge paradigm appears to have a long future, but no one was dreaming of this approach when we were building qpm only 25 years ago. to illustrate, an alternative approach worth exploring may be agent - based models. unlike the dsge approach, agent - based models assume that the economy consists of individuals who repeatedly interact with each other and adapt their behaviour based on what they learn from those experiences. macroeconomic behaviour emerges naturally. such models have their own limitations, but in a world of big data, where the advertisements you see online can be derived from what you type into your search