A long return history is useful in estimating the current equity premium even if the historical distribution has experienced structural breaks. The long series helps not only if the timing of breaks is uncertain but also if one believes that large shifts in the premium are unlikely or that the...

This paper proposes a method for separating economic time series into a smooth component whose mean varies over time (the trend') and a stationary component (the cycle'). The aim is to make the trends as smooth as possible while also producing cycles with plausible properties. While the main...

The `ideal' band pass filter can be used to isolate the component of a time series that lies within a particular band of frequencies. However, applying this filter requires a dataset of infinite length. In practice, some sort of approximation is needed. Using projections, we derive...

A time series is concentrated if the expectation of its current value is a negative function of a moving average of past values up to all but the most recent past. Job destruction has the property of concentration in a model of heterogeneous jobs because an adverse shock destroys jobs in plants...

This paper presents evidence on attitude changes among investors in the US stock market. Two basic attitudes are explored: bubble expectations and investor confidence. Semiannual time-series indicators of these attitudes are presented for US stock market institutional investors based on...

Fluctuations in real GNP have traditionally been viewed as transitory deviations from a deterministic time trend. The purpose of this paper is to review some of the recent developments that have led to a new view of output fluctuations and then to provide some additional evidence. Using post-war...

We establish that the recursive, state-space methods of Kalman filtering and smoothing can be used to implement the Doan, Litterman, and Sims (1983) approach to econometric forecast and policy evaluation. Compared with the methods outlined in Doan, Litterman, and Sims, the Kalman algorithms are...