| dc.description.abstract | This thesis proposes a new econometric methodology for the estimation and inference of macro-
economic models in the presence of time variation in the parameters. A novel quasi-Bayesian
local likelihood (QBLL) approach is established and it is shown that the method gives rise to as-
ymptotically valid quasi-posterior distributions. In addition, in the special case of linear Gaussian
models, expressions of the quasi-posteriors are derived in closed form, which simpli es inference and
makes the use of MCMC unnecessary. Inference based on the QBLL approach, as a consequence
of modelling parameter variation nonparametrically, is robust to di¤erent processes for the drifting
parameters, as its validity does not depend on parametric restrictions typically imposed by alterna-
tive state space models. In addition, the Bayesian treatment of the approach provides a remedy to
the curse of dimensionality by accommodating large dimensional systems. We demonstrate that
the proposed estimators exhibit good nite sample properties, and, unlike the alternative para-
metric state space models, are robust to di¤erent parameter processes. We provide a variety of
interesting macroeconomic applications and forecasting exercises to reduced-form VAR models. In
addition, we develop the methodology to the estimation of structural DSGE models in the presence
of parameter drift. We apply the proposed algorithms to di¤erent medium-sized DSGE models in
order to study structural change in the parameters. | en_US |
