A Quasi-Bayesian Local Likelihood Approach to Time Varying Parameter Models
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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.
- Theses