We provide a theoretical and empirical analysis of the link between financial and real health care markets. This link is important as financial returns drive investment in medical research and development (R&D), which, in turn, affects real spending growth. We document a “medical innovation premium” of 4–6% annually for equity returns of firms in the health care sector. We interpret this premium as compensating investors for government-induced profit risk, and we provide supportive evidence for this hypothesis through company filings and abnormal return patterns surrounding threats of government intervention. We quantify the implications of the premium for the growth in real health care spending by calibrating our model to match historical trends, predicting the share of gross domestic product (GDP) devoted to health care to be 32% in the long run. Policies that had removed government risk would have led to more than a doubling of medical R&D and would have increased the current share of health care spending by more than 3% of GDP.

This paper studies how the Hodrick-Prescott filter should be adjusted when changing the frequency of observations. It complements the results of Baxter and King (1999) with an analytical analysis, demonstrating that the filter parameter should be adjusted by multiplying it with the fourth power of the observation frequency ratios. This yields an HP parameter value of 6.25 for annual data given a value of 1600 for quarterly data. The relevance of the suggestion is illustrated empirically.

This paper examines the role for tax policies in productivity-shock driven economies with catching-up-with-the-Joneses utility functions. The optimal tax policy is shown to affect the economy countercyclically via procyclical taxes, i.e., “cooling down” the economy with higher taxes when it is “overheating” in booms and “stimulating” the economy with lower taxes in recessions to keep consumption up. Thus, models with catching-up-with-the-Joneses utility functions call for traditional Keynesian demand-management policies but for rather unorthodox reasons.

This paper studies decision-making with rules of thumb in the context of dynamic decision problems and compares it to dynamic programming. A rule is a fixed mapping from a subset of states into actions. Rules are compared by averaging over past experiences. This can lead to favoring rules which are only applicable in good states. Correcting this good state bias requires solving the dynamic program. We provide a general framework and characterize the asymptotic properties. We apply it to provide a candidate explanation for the sensitivity of consumption to transitory income.

We develop a simple model in which the presence of portfolio insurers in a market of risk‐averse traders leads to multiple equilibria for the pricing of financial assets and can cause an increase in volatility, including insurance‐induced price drops. We demonstrate, however, that centralized portfolio insurance firms may actually reduce, not increase, volatility, even if the existence of these firms increases the total amount of funds under insurance.