Abstract Since the Great Recession in 2007–2009, U.S. real GDP has failed to return to its previously projected path, a phenomenon widely associated with secular stagnation. We investigate whether this stagnation was due to hysteresis effects from the Great Recession, a persistent negative output gap following the recession, or slower trend growth for other reasons. To do so, we develop a new Markov-switching time series model of output growth that accommodates two different types of recessions: those that permanently alter the level of real GDP and those with only temporary effects. We also account for structural change in trend growth. Estimates from our model suggest that the Great Recession generated a large, persistent negative output gap rather than any substantial hysteresis effects, with the economy eventually recovering to a lower trend path that appears to be due to a reduction in productivity growth that began prior to the onset of the Great Recession.

The business cycle is a fundamental yet elusive concept in macroeconomics. In this paper, we consider the problem of measuring the business cycle. First, we argue for the output-gap view that the business cycle corresponds to transitory deviations in economic activity away from a permanent, or trend, level. Then we investigate the extent to which a general model-based approach to estimating trend and cycle for the U.S. economy leads to measures of the business cycle that reflect models versus the data. We find empirical support for a nonlinear time series model that produces a business cycle measure with an asymmetric shape across NBER expansion and recession phases. Specifically, this business cycle measure suggests that recessions are periods of relatively large and negative transitory fluctuations in output. However, several close competitors to the nonlinear model produce business cycle measures of widely differing shapes and magnitudes. Given this model-based uncertainty, we construct a model-averaged measure of the business cycle. This measure also displays an asymmetric shape and is closely related to other measures of economic slack such as the unemployment rate and capacity utilization.

Abstract The Beveridge-Nelson decomposition based on autoregressive models produces estimates of the output gap that are strongly at odds with widely held beliefs about transitory movements in economic activity. This is due to parameter estimates implying a high signal-to-noise ratio in terms of the variance of trend shocks as a fraction of the overall forecast error variance. When we impose a lower signal-to-noise ratio, the resulting Beveridge-Nelson filter produces a more intuitive estimate of the output gap that is large in amplitude and highly persistent, and it typically increases in expansions and decreases in recessions. Notably, our approach is also reliable in the sense of being subject to smaller revisions and predicting future output growth and inflation better than other trend-cycle decompositions that impose a low signal-to-noise ratio.

This paper reconciles two widely used decompositions of GDP into trend and cycle that yield starkly different results. The Beveridge-Nelson (BN) decomposition implies that a stochastic trend accounts for most of the variation in output, whereas the unobserved-components (UC) implies cyclical variation is dominant. Which is correct has broad implications for the relative importance of real versus nominal shocks. We show the difference arises from the restriction imposed in UC that trend and cycle innovations are uncorrelated. When this restriction is relaxed, the UC decomposition is identical to the BN decomposition. Furthermore, the zero-correlation restriction can be rejected for U.S. quarterly GDP, with the estimated correlation being -0.9.