Dear all,

I am trying to estimate Laubach & Wiliams(2003) type model and incorporate Covid-related large volatility in the model.

Holston, Laubach and Williams(2023) introduced time-varying volatility by multiplying a scale factor \kappa_{t} to the shock variances of the model’s measurement equations. In their specification, \kappa_{t} is greater than 1 during the pandemic years(2020~2022) and the innovation variance increases.

When introducing the scale factor in my dynare code, the posterior estimates for KAPPA and covid related parameters seems not to reflect the information of data.

(i.e. posterior distribution is almost the same as prior distribution I considered, regardless of the type of distribution(e.g. normal, beta, inverse gamma,…))

I made \kappa_{t} be the linear sum of year dummies d_2020, d_2021, d_2022 as follows :

KAPPA = ( 1+ k2020*d_2020 + k_2021*d_2021 + k_2022*d_2022 )

How can I deal with this kind of time-varying volatility using dynare?

If detailed sample code script is needed, please see the attached files.

LW_covid.mod (5.7 KB)

LW_covid_data.mat (7.2 KB)

main_LW_covid.m (684 Bytes)