How accurate are real-time estimates of output trends and gaps?

Macroeconomics

Market anticipations of monetary policy actions - commentary

Monetary policy ; Federal funds rate

Assessing changes in the monetary transmission mechanism: a VAR approach : commentary

Paper for a conference sponsored by the Federal Reserve Bank of New York entitled Financial Innovation and Monetary TransmissionEconomic conditions - United States ; Monetary policy

Explaining the increased variability in long-term interest rates

Interest rates ; Federal funds rate

Relative Goods' Prices, Pure Inflation, and the Phillips Correlation

This paper uses a dynamic factor model for the quarterly changes in consumption goods’ prices to separate them into three independent components: idiosyncratic relative-price changes, a low-dimensional index of aggregate relative-price changes, and an index of equiproportional changes in all inflation rates, that we label “pure” inflation. The paper estimates the model on U.S. data since 1959, and it presents a simple structural model that relates the three components of price changes to fundamental economic shocks. We use the estimates of the pure inflation and aggregate relative-price components to answer two questions. First, what share of the variability of inflation is associated with each component, and how are they related to conventional measures of monetary policy and relative-price shocks? We find that pure inflation accounts for 15-20% of the variability in inflation while our aggregate relative-price index accounts most of the rest. Conventional measures of relative prices are strongly but far from perfectly correlated with our relative-price index; pure inflation is only weakly correlated with money growth rates, but more strongly correlated with nominal interest rates. Second, what drives the Phillips correlation between inflation and measures of real activity? We find that the Phillips correlation essentially disappears once we control for goods’ relative-price changes. This supports modern theories of inflation dynamics based on price rigidities and many consumption goods.

Testing Models of Low-Frequency Variability

We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle.

Has the Business Cycle Changed and Why?

From 1960-1983, the standard deviation of annual growth rates in real GDP in the United States was 2.7%. From 1984-2001, the corresponding standard deviation was 1.6%. This paper investigates this large drop in the cyclical volatility OF real economic.activity. The paper has two objectives. The first is to provide a comprehensive characterization of the decline in volatility using a large number of U.S. economic time series and a variety of methods designed to describe time-varying time series processes. In so doing, the paper reviews the literature on the moderation and attempts to resolve some of its disagreements and discrepancies. The second objective is to provide new evidence on the quantitative importance of various explanations for this 'great moderation.' Taken together, we estimate that the moderation in volatility is attributable to a combination of improved policy (20-30%), identifiable good luck in the form of productivity and commodity price shocks (20-30%), and other unknown forms of good luck that manifest themselves as smaller reduced-form forecast errors (40-60%).

Seasonal Adjustment with Measurement Error Present

Seasonal adjustment procedures attempt to estimate the sample realizations of an unobservable economic time series in the presence of both seasonal factors and irregular factors. In this paper we consider a factor which has not been considered explicitly in previous treatments of seasonal adjustment: measurement error. Because of the sample design used in the CPS, measurement error will not be a white noise process, but instead it will be characterized by serial correlation of a known form. We first consider what effect the serially correlated measurement error has on estimation of the non-seasonal component in seasonal adjustment models. We also consider the effect of measurement error on the widely used seasonal adjustment process X11. X11 which is the seasonal adjust procedure used by the BLS will implicitly reduce the effect of measurement error because of the averaging process used. However, this treatment will not be optimal in general. We therefore specify a seasonal adjustment model which takes explicit account of the measurement error. For examples on the unemployment rate, we find that X11 does almost as well as the optimal filter on some series but its efficiency is less than 10% for the teenage unemployment series. We also find that optimal treatment of the measurement error which accounts for the serial correlation can reduce the overall mean square error of the seasonally adjusted series below the variance of the measurement error which is often used as the benchmark for the sampling procedure.