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DTSTAMP:20190719T085743Z
LOCATION:HG D 3.2
DTSTART;TZID=Europe/Stockholm:20190612T140000
DTEND;TZID=Europe/Stockholm:20190612T143000
UID:submissions.pasc-conference.org_PASC19_sess124_msa109@linklings.com
SUMMARY:Efficient Solution, Filtering and Estimation of Models with OBCs
DESCRIPTION:Minisymposium\nEmerging Application Domains\n\nEfficient Solut
 ion, Filtering and Estimation of Models with OBCs\n\nBoehl\n\nOccasionally
  binding constraints (OBCs) play a central role in macroeconomic modelling
  since major developed economies have hit the effective lower bound (ELB) 
 on interest rates. I propose a solution method for rational expectations m
 odels with OBCs and a Bayesian filter/smoother that, both combined, can be
  used for quick and accurate Bayesian estimation of large-scale models. Th
 e quasi-analytic solution method calculates the endogenous duration at the
  constraint while avoiding matrix inversions and simulations at runtime fo
 r gains in computational speed. The IPA (iterative path-adjusting transpos
 ed-ensemble RTS) smoother is a hybrid form of the particle filter and iter
 ative versions of the Kalman filter. Requiring only a very small number of
  particles, it can be used to approximate the likelihood with high accurac
 y, and applied to estimate the state distributions while fully respecting 
 the nonlinearity of the transition function. As an example, I use these me
 thods to estimate the simple New Keynesian model.
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