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This article demonstrates the importance of recessions in driving down the r* estimate produced by the Holsten-Laubach-Williams (HLW) methodology. Although there are other algorithms that can be used to generate a r* estimate, my argument is that they should have similar qualitative properties. In the case of the HLW estimate, my argument is that the nature of the recession in 2008 is a major contributor to the fall in r* thereafter. The underlying problem is that real-world data does not match the probability distribution assumed in the algorithm.Bond EconomicsEffect Of Recessions On r* EstimatesBrian Romanchuk
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This article demonstrates the importance of recessions in driving down the r* estimate produced by the Holsten-Laubach-Williams (HLW) methodology. Although there are other algorithms that can be used to generate a r* estimate, my argument is that they should have similar qualitative properties. In the case of the HLW estimate, my argument is that the nature of the recession in 2008 is a major contributor to the fall in r* thereafter. The underlying problem is that real-world data does not match the probability distribution assumed in the algorithm.Bond EconomicsEffect Of Recessions On r* EstimatesBrian Romanchuk
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Mike Norman considers the following as important:
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This article demonstrates the importance of recessions in driving down the r* estimate produced by the Holsten-Laubach-Williams (HLW) methodology. Although there are other algorithms that can be used to generate a r* estimate, my argument is that they should have similar qualitative properties. In the case of the HLW estimate, my argument is that the nature of the recession in 2008 is a major contributor to the fall in r* thereafter. The underlying problem is that real-world data does not match the probability distribution assumed in the algorithm.Bond Economics
Effect Of Recessions On r* Estimates
Brian Romanchuk