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10.1016/j.jeconom.2020.08.008 http://scihub22266oqcxt.onion/10.1016/j.jeconom.2020.08.008 33012953!7519716!33012953     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Econom 2021 ; 220 (1): 158-180 Nephropedia Template TP {{tp|p=33012953|t=2021. Sparse HP filter: Finding kinks in the COVID-19 contact rate |pdf=|usr=}}{{33012953}} gab.com Text Sparse HP filter: Finding kinks in the COVID-19 contact rate JO: J Econom http://www.kidney.de/pget.php?&tw=1&pmid=33012953 #FOAvip In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate is constructed using data on actively infected, recovered and deceased cases. We propose a new trend filtering method that is a variant of the Hodrick-Prescott (HP) filter, constrained by the number of possible kinks. We term it the sparse HP filter and apply it to daily data from five countries: Canada, China, South Korea, the UK and the US. Our new method yields the kinks that are well aligned with actual events in each country. We find that the sparse HP filter provides a fewer kinks than the l1 trend filter, while both methods fitting data equally well. Theoretically, we establish risk consistency of both the sparse HP and l1 trend filters. Ultimately, we propose to use time-varying contact growth rates to document and monitor outbreaks of COVID-19. Twit Text FOAVip Sparse HP filter: Finding kinks in the COVID-19 contact rate ????J Econom http://www.kidney.de/pget.php?&tw=1&pmid=33012953 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33012953 #FOAvip English Wikipedia <ref>{{Cite pmid|33012953}}</ref> Sparse HP filter: Finding kinks in the COVID-19 contact rate #MMPMID33012953 Lee S; Liao Y; Seo MH; Shin Y J Econom 2021[Jan]; 220 (1): 158-180 PMID33012953show ga In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate is constructed using data on actively infected, recovered and deceased cases. We propose a new trend filtering method that is a variant of the Hodrick-Prescott (HP) filter, constrained by the number of possible kinks. We term it the sparse HP filter and apply it to daily data from five countries: Canada, China, South Korea, the UK and the US. Our new method yields the kinks that are well aligned with actual events in each country. We find that the sparse HP filter provides a fewer kinks than the l1 trend filter, while both methods fitting data equally well. Theoretically, we establish risk consistency of both the sparse HP and l1 trend filters. Ultimately, we propose to use time-varying contact growth rates to document and monitor outbreaks of COVID-19. äSparse HP filter: Finding kinks in the COVID-19 contact rate (epmc).pdf DeepDyve Pubget Overpricing