I see that gretl uses by default the value lambda=1600 for quarterly data, lambda = 100 for annual data and lambda=14400 for monthly data. For the quarterly case this was studied in the HP article and this value has been adopted almost as a "convention" for obtaining a trend/cycle decomposition. The values for annual and monthly data has been during some time subject to controversy. In particular, some papers consider the above values inappropiate because the agregation(from monthly to quarterly or quarterly to annual) of the trend gives very different results. But now, based on this requirement of equivalence on agregation, it seems that finally an agreement has been reached. It implies lambda(annual)=6.25 and lambda(monthly)=129600 or more generally lambda(S)=6.25*S^4 being S the seasonal periodicity. I suggest that gretl adopts these defaults. In these papers we have different justifications for such a formula: Maravall, A. y del Rı̀o, A. (2001), Time aggregation and the Hodrick- Prescott filter, Working paper 0108, Banco de España. Ravn, M. O. y Uhlig, H. (2002), ‘On adjusting the Hodrick-Prescott filter for the frequency of observations’, The Review of Economics and Statistics 84(2), 371–380. de Jong, R. M. y Sakarya, N. (2016), ‘The econometrics of the Hodrick- Prescott filter’, The Review of Economics and Statistics 98(2), 310–317. -- Ignacio Díaz-Emparanza Departamento de Economía Aplicada III (Econometría y Estadística) Universidad del País Vasco - Euskalherriko Unibertsitatea, UPV/EHU Tfno: (+34) 94 601 3732 http://www.ehu.eus/ea3 _______________________________________________ Gretl-devel mailing list Gretl-devel@lists.wfu.edu http://lists.wfu.edu/mailman/listinfo/gretl-devel