This paper investigates the nature of the Federal Open Market Committee's (FOMC's) interest rate rule, with a focus on which variables have been relevant to the FOMC over the past 40 years. I consider a large number of potential variables, including alternate measures of inflation, aggregate real activity, and sectoral variables. Based on inclusion probabilities derived from Bayesian Model Averaging (BMA) over a sample from 1970-2007, I find that the FOMC responds to changes in unemployment rather than to changes in GDP growth. Additionally, I find that the FOMC reacts not only to inflation and aggregate output, but also to measures of sectoral activity, such as changes in commodity prices. Finally, I find that using BMA improves out-of-sample forecasting performance over baseline Taylor-type interest rate rules.
Many researchers and economic commentators believe that the Federal Open Market Committee (FOMC) has changed interest rate policy over time. Mathematically, FOMC policy is typically described as a linear interest rate rule, in which the FOMC changes interest rates based on measure of the inflation gap and a measure of the output gap. In this context, a change in policy is associated with a change in the coefficients of this rule. I introduce a new econometric model in order to determine if there have been changes in these coefficients. I call this model a Markov Switching Stochastic Search Variable Selection (MS-SSVS) model, as it builds on the work of George and McCulloch (1993) who introduced SSVS in linear models, and also on the work of Hamilton (1989), Kim and Nelson (1999), Fruwirth-Schnatter (2006) and others who have popularized the use of Markov-Switching models. This model embeds linear regression and Markov-Switching models as special cases, as it allows regression coefficients to be freely estimated, restricted to be identical across regimes, or restricted to be equal to zero. Because it builds these features into the likelihood function of a single model, estimation is fast and does not involve the approximation of marginal likelihoods. I find that there is very little evidence of coefficient change, with the data suggesting only a change in response to the unemployment gap.
I apply a recently developed Markov Switching Time-Varying Parameter (MS-TVP) model to test for bubbles in asset markets. In particular, I adapt the model put forth in Eo and Kim (2012), which takes advantage of the use of hierarchical priors governing the evolution of time-varying parameters in a Markov switching model, to the Augmented Dickey-Fuller (ADF) test for asset bubbles proposed in Hall et al. (1999). This paper expands on the prior literature in two important directions. First, it introduces Bayesian estimation and inference to Hall et al.'s (1999) ADF bubble test. Next, it allows the parameters in the Hall et al. (1999) model to change upon entering each episode of a high return and slow return regime. I find that for periodically collapsing bubbles generated according to the process introduced in Evans (1991), both the MS-TVP and the Bayesian Hall et al. (1999) tests have similar power to detect bubbles.