| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Outline of Final Research Achievements |
This research project intends to introduce a new trend filtering method, which has the Hodrick-Prescott (HP) filtering method of Hodrick and Prescott (1997, Journal of Money, Credit and Banking, 29, 1, 1-16) and the l1 trend filtering method of Kim et al. (2009, SIAM Review, 51, 339-360) as special cases. The relation between the HP filtering method, the l1 trend filtering method, and the new filtering method corresponds to the relation between the ridge regression, the lasso (least absolute shrinkage and selection operator) regression, and the bridge regression. For this reason, we refer to the new filtering method as bridge filtering method. The bridge filtering method enables us to estimate the trend component of a time series with less-sudden structural changes. In this project, after introducing the new filtering method, we show some of its properties, a method for specifying its tuning parameter, and an empirical illustration of how it may be applied.
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