|dc.description.abstract||The recent financial and economic turmoil driven by housing market has led the economists to refocus on the issue about monetary policy and asset price, especially housing price. In this dissertation I investigate the various relationships between monetary policy and asset prices in U.S. economy through steady state Bayesian VAR (SS BVAR) and revised Taylor-typed interest rate rule (Forward-looking rule) based on Generalized Method of Moments (GMM) methodology.
In chapter II, steady state Bayesian VAR (SS BVAR) methodology is introduced and multi step-ahead forecasts are executed. Upon usual squared error loss methodology the forecasting performances of SS BVAR are evaluated in comparison with standard BVAR and conventional VAR. Equal predictive ability tests following Giacomini and White (2006) verify that the SS BVAR is superior in forecasting power especially in long-horizons.
In chapter III, identification issue involving housing sector is explored through two different ways: economic theory-based approach and algorithms of inductive causations. Despite the different approaches the housing sector?s specifications are somewhat similar. Impulse response analyses demonstrate that monetary shock to housing price is relatively smaller, less significant, and less lasting when compared to Choleski identification. Also historical decomposition and conditional forecast analyses indicate that the housing price shock itself is crucial in accounting the sharp increase and sudden drop of housing price since 2003. Upon the estimated evidences I conjecture that there are much uncertainty between monetary policy and housing price, recalling the consideration of institutional factors when trying to accounting housing sectors.
In chapter IV, following Dupor and Conley (2004), I explore how Fed responds to stock price and inflation movements differently across high and low inflation sub-periods. Replicated linear estimation results of Dupor and Conley (2004)?s indicate that Fed raises its target interest rate responding to stock price gap with statistical significance. Linear estimation results, however, are not robust to small change of chosen breakpoint especially in inflation coefficient. So I construct nonlinear model as an alternative way to relax this problem and carry out test of structural change with the nonlinear framework. Consequently both nonlinearity and structural change matter in explanation of Fed?s behavior in this type of reaction function analysis. Given structural change, inflation coefficients movement shows that Fed has responded to expected inflation pressure nonlinearly across sub-period, while stock price gap coefficient shows explicit break around early ?90 in line with Dupor and Conley (2004)?s finding.||