Average postwar expansions are twice as long as prewar expansions, and contractions are one-half as long. This paper investigates three possible explanations. The first explanation is that shocks to the economy have been smaller in the postwar period. The second explanation is that the composition of output has shifted from very cyclical sectors to less cyclical sectors. The third explanation is that the apparent stabilization is largely spurious and is caused by differences in the way that prewar and postwar business-cycle reference dates were chosen by the NBER. The evidence presented in this paper favors this third explanation.

This paper suggests a new procedure for evaluating the fit of a dynamic structural economic model. The procedure begins by augmenting the variables in the model with just enough stochastic error so that the model can exactly match the second moments of the actual data. Measures of fit for the model can then be constructed on the basis of the size of this error. The procedure is applied to a standard real business cycle model. Over the business cycle frequencies, the model must be augmented with a substantial error to match data for the postwar U.S. economy. Lower bounds on the variance of the error range from 40 percent to 60 percent of the variance in the actual data.

We develop a framework to assess how successfully standard time series 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. Copyright 2008 The Econometric Society.

The rate of inflation fell far less over the period 2007-2013 than in the period 1979-1985 despite similar large increases in the unemployment rate. This paper asks why. Possible explanations include a change in the persistence of inflation, changes in NAIRU, and other shocks. A change in the persistence of inflation, with inflation more anchored in the period 2007-2013 than in the period 1979-1985, is found to be important. The level and change in the NAIRU cannot be precisely estimated, but the data suggest an increase of nearly 1 percentage point since 2007.

This paper suggests a new procedure for evaluating the fit of a dynamic structural economic model. The procedure begins by augmenting the variables in the model with just enough stochastic error so that the model can exactly match the second moments of the actual data. Measures of fit for the model can then be constructed on the basis of the size of this error. The procedure is applied to a standard real business cycle model. Over the business cycle frequencies, the model must be augmented with a substantial error to match data for the postwar U.S. economy. Lower bounds on the variance of the error range from 40 percent to 60 percent of the variance in the actual data.

This paper examines old and new evidence on the predictive performance of asset prices for inflation and real output growth. We first review the large literature on this topic, focusing on the past dozen years. We then undertake an empirical analysis of quarterly date on up to 38 candidate indicators (mainly asset prices) for seven OECD countries for a span of up to 41 years (1959 - 1999). The conclusions from the literature review and the empirical analysis are the same. Some asset prices predict either inflation or output growth in some countries in some periods. Which series predicts what, when and where is, however, itself difficult to predict: good forecasting performance by an indicator in one period seems to be unrelated to whether it is a useful predictor in a later period. Intriguingly, forecasts produced by combining these unstable individual forecasts appear to improve reliably upon univariate benchmarks.

Are asset prices useful predictors of inflation and real output growth? After reviewing the large literature on this topic, we undertake an empirical analysis of quarterly data for seven OECD countries spanning 1959-99. The literature review and the empirical analysis yield the same conclusions. Some asset prices predict inflation or output growth in some countries in some periods. Which series predicts what, when, and where is difficult to predict; being a good predictor historically is largely unrelated to subsequent performance. Intriguingly, forecasts that combine these individually unstable forecasts appear to improve reliably upon univariate benchmarks.

This paper studies the problems of estimation and inference in the linear trend model yt = α + βt + ut, where ut follows an autoregressive process with largest root ρ and β is the parameter of interest. We contrast asymptotic results for the cases | ρ | < 1 and ρ = 1 and argue that the most useful asymptotic approximations obtain from modeling ρ as local to unity. Asymptotic distributions are derived for the OLS, first-difference, infeasible GLS, and three feasible GLS estimators. These distributions depend on the local-to-unity parameter and a parameter that governs the variance of the initial error term κ. The feasible Cochrane–Orcutt estimator has poor properties, and the feasible Prais–Winsten estimator is the preferred estimator unless the researcher has sharp a priori knowledge about ρ and κ. The paper develops methods for constructing confidence intervals for β that account for uncertainty in ρ and κ. We use these results to estimate growth rates for real per-capita GDP in 128 countries.