The prodigious level of spending makes health care important, but it is the fearsome growth of health care costs that has put health care reform on the national agenda. Medicare spending has grown 2.4 percentage points per year faster than GDP over the past 30 years, more than tripling as a share of GDP since 1960. If costs continue to grow at current rates relative to GDP, then Medicare alone will account for 8 percent of GDP by 2030, 14 percent by 2050, and 31 percent by the end of the 75-year budget projection window. This would imply that Medicare would be more than 50 percent larger than the current size of the entire federal budget. While these numbers are absurdly large, a similar projection would forecast total health care spending to account for an impossible 99 percent of GDP by 2082 (Congressional Budget Office, 2007).

But if health care spending does not continue to rise at historical rates, then what will happen instead? How drastic must reform be to avert this looming fiscal crisis? The magnitude of the situation requires people to understand the possible paths forward for health care spending as never before. But the sheer speed of past cost growth that demands a policy intervention has simultaneously rendered moot the most direct and widely used forecast strategy of projecting forward past growth.

In order to solve this forecasting problem, both the academic literature and relevant government agencies have devoted considerable time, creative energy, and resources to developing models of Medicare cost growth. In the academic literature, researchers have developed a number of strategies to forecast cost growth. Below I discuss three main approaches: extrapolation, microsimulation, and computable general equilibrium models. I review the mechanics of each approach, as well as their strengths and weaknesses. I pay particular attention to the assumptions on the dynamics of health care demand, health care supply, and technological growth as factors driving costs. Extrapolation and microsimulation are fundamentally statistical or actuarial in nature, while general equilibrium models focus instead on the economic dynamics of health care cost growth. Each forecasting approach also has different strengths over the long and short run.

I then discuss the particular applications of one or more of these forecasting methods in important policy contexts. Each government agency combines these three methods in different ways when producing the numbers on which policy is formally based. For instance, the Office of the Actuary (OACT) combines extrapolation with a computable general equilibrium model, whereas the Congressional Budget Office (CBO) relies on constrained extrapolation. Drawing on the more abstract methodological discussion of the literature, I discuss the practical implications, as well as strengths and weaknesses of each approach. Since each approach from the literature presents different strengths and weaknesses, policy makers often modify the precise forecasting strategy depending on the particular context

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