Recent research shows that when commonly estimated dynamic Taylor rules, which are augmented with a lagged interest, are embedded in a variety of macroeconomic models, they imply a greater amount of predictable information about future movements in interest rates than is actually evident in the yield curve. We extend the analysis to consider more generally the predictability of the arguments of the Taylor rule - inflation and the output gap - in addition to the interest rate. Specifically, we compare the predictability of these three variables in a macroeconomic model with a dynamic Taylor rule to their predictability in real-time surveys of macroeconomic forecasters or a VAR model. We find that the strongest evidence against the dynamic Taylor rule is that while it is easy to predict the variables that enter the rule, it is very hard to predict actual interest rate changes. This disparity suggests that dynamic Taylor rules neglect important aspects of monetary policy behaviour.
This paper is a selective survey of new or recent methods to extract information about market expectations from asset prices for monetary policy purposes. Traditionally, interest rates and forward exchange rates have been used to extract expected means of future interest rates, exchange rates and inflation. More recently, these methods have been refined to rely on implied forward interest rates, so as to extract expected future time-paths. Very recently, methods have been designed to extract not only the means but the whole (risk neutral) probability distribution from a set of option prices.
A simple consumption-based two-period model is used to study the (theoretical) effects of disagreement on asset prices. Analytical and numerical results show that individual uncertainty has a much larger effect on risk premia than disagreement if (i) the risk aversion is reasonably high and (ii) individual uncertainty is not much smaller than disagreement. Evidence from survey data on beliefs about output growth suggests that the latter is more than satisfied.
A structural rational expectations model of U.S. monetary policy is used to make a counterfactual experiment of a strongly inflation averse Federal Reserve Bank. Results for U.S. interest rates, output, and inflation over 1965-1999 are discussed.
A GMM-based system for two different linear factor pricing models is used to test if the pricing errors are the same. Simulations demonstrate the small sample properties. As an illustration, the test is applied to the Fama-French (1996, 2015) models.
Macro models of monetary policy typically involve forward looking behavior. Except in rare circumstances, we have to apply some numerical method to find the optimal policy and the rational expectations equilibrium. This paper summarizes a few useful methods, and shows how they can be combined with a Kalman filter to estimate the deep model parameters with maximum likelihood. Simulations of a macro model with staggered price setting, interest rate elastic output, and optimal monetary policy illustrate the properties of this estimation approach.
An affine yield curve model is estimated on daily Swiss data 2002-2009. The market price of risk is modelled in terms of proxies for uncertainty, which are estimated from interest rate options. The estimated model generates innovations in the 3-month rate that are similar to external evidence of monetary policy surprises - as well as term premia that are consistent with survey data. The results indicate that a surprise increase in the policy rate gives a reasonably sized decrease (-0.25%) in term premia for longer maturities.
The forecasting performance of the Livingston survey and traditional prediction models of stock prices is analysed. The survey forecasts look similar to those from a ‘too large' prediction model: poor out-of-sample performance and too sensitive to recent and irrelevant information.
