By John Y. Campbell
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See, for example, Norman, Sebastia-Barriel, and Weeken (2002). 20. The elasticity of consumption with respect to wealth is equal to the marginal propensity to consume out of wealth times the ratio of wealth to consumption. 26 Stephen G. 3 reports the coeﬃcient b3 in the regression xit ϭ a ϩ b1dit–k(␣) ϩ b2tail(␤)it ϩ b3dit–k(␣)x tailit(␤) ϩ υit, where xit is the deviation of either log GDP or the log price from a Hodrick-Prescott filtered trend, with parameter 1,600; tailit(␤) is a dummy variable that equals 1 if xit in the lower ␤-percent tail; and d is either a dummy variable equal to 1 if the filtered asset price exceeds the threshold ␣ (in percent), or the filtered asset price data itself (those are the rows labeled “data”).
4. The results in these two figures suggest that housing booms are followed by an increased risk of a large decline in GDP in four to twelve quarters and a decreased risk of prices falling below trend. Note from the scale that the GDP at risk is quite large. When real house prices are 5 percent or more above trend, there is a Measuring the Macroeconomic Risks Posed by Asset Price Booms 21 A B Fig. 16 Housing booms aﬀect the price level at risk as well. 6 suggests that a housing boom has very little impact on the upper tail of the price-level distribution, but dramatically eliminates the lower tail—at least at a twelve-quarter horizon.
S. homebuilder industry along with an overview of Chicago Mercantile Exchange (CME) housing futures and options, while Campbell et al. S. house prices can be interpreted in terms of movements in rents, real interest rates, and risk premia. Measuring the Macroeconomic Risks Posed by Asset Price Booms 41 Fig. S. S. firms in the residential construction industry. S. house prices over the subsequent few quarters. , A. Levin, R. Tryon, and J. Williams. 1997. The evolution of macro models at the Federal Reserve Board.