Uzeda Garcia, Luis Henrique
Description
After the introductory chapter, this thesis comprises two further
chapters. The main chapters in this dissertation, i.e., chapters
2 and 3 are presented in essay format, each with an independent
introduction and conclusion. The contents of these individual
chapters are outlined below.
Chapter 2 studies the forecasting implications of specifying
unobserved components (UC) models with different state
correlation structures. While implications for signal...[Show more] extraction
from specifying UC models with correlated or orthogonal
innovations have been well-investigated, out-ofsample
implications are less well understood. This paper attempts to
address this gap in light of the recent resurgence of studies
adopting UC models for forecasting purposes. Four correlation
structures for errors are entertained: orthogonal, correlated,
perfectly correlated innovations as well as a novel approach
which combines features from two contrasting cases, namely,
orthogonal and perfectly correlated innovations. Parameter space
restrictions associated with different correlation structures and
their connection with forecasting are discussed within a Bayesian
framework. As perfectly correlated innovations reduce the
covariance matrix rank, a Markov Chain Monte Carlo sampler which
builds upon properties of Toeplitz matrices and recent advances
in precision-based algorithms is developed. Our results for
several measures of U.S. inflation indicate that the correlation
structure between state variables has important implications for
forecasting performance as well as estimates of trend inflation.
Chapter 3 develops an econometric framework to investigate the
contribution of monetary policy to the evolution of U.S. trend
inflation. We combine two modeling approaches - measuring trend
inflation using an unobserved components model and estimation of
monetary policy rules with drifting coefficients - to investigate
interdependence between policy rule parameters and trend
inflation. We employ identification strategies of the policy
shock to trend inflation which highlight particular changes in
the conduct of systematic monetary policy and overidentify a
state space model for inflation and the policy rate. An effcient
Markov Chain Monte Carlo algorithm using precision-based methods
is proposed for static and dynamic selection of policy drivers
behind trend inflation. Our empirical analysis indicates three
main results: (1) the influence of monetary policy on trend
inflation increased during the Great Moderation relative to the
Great Inflation period; (2) non-policy shocks, however, accounted
for between 50 and 70 per cent of the variation in trend
inflation during each of these episodes; (3) monetary policy's
contribution to stabilize trend inflation around the early 1980s
reflects a weaker reaction to output gap changes accompanied by a
stronger emphasis on inflation gap dynamics and inflation target
adjustments.
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.