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Working Paper
Nonlinearity and the permanent effects of recessions
This paper presents a new nonlinear time series model that captures a post-recession ?bounce-back? in the level of aggregate output. While a number of studies have examined this type of business cycle asymmetry using recession-based dummy variables and threshold models, we relate the ?bounce-back? effect to an endogenously estimated unobservable Markov-switching state variable. When the model is applied to U.S. real GDP, we find that the Markov-switching regimes are closely related to NBER-dated recessions and expansions. Also, the Markov-switching form of nonlinearity is statistically ...
Working Paper
A Structural Measure of the Shadow Federal Funds Rate
We propose a shadow policy interest rate based on an estimated structural model that accounts for the zero lower bound. The lower bound constraint, if expected to bind, is contractionary and increases the shadow rate compared to an unconstrained systematic policy response. By contrast, forward guidance and other unconventional policies that extend the expected duration of zero-interest-rate policy are expansionary and decrease the shadow rate. By quantifying these distinct effects, our structural shadow federal funds rate better captures the stance of monetary policy given economic conditions ...
Working Paper
In search of the natural rate of unemployment
The natural rate of unemployment can be measured as the time-varying steady state of a structural vector autoregression. For post-War U.S. data, the natural rate implied by this approach is more volatile than most previous estimates, with its movements accounting for the bulk of the variation in the unemployment rate, as well as substantial portions of the variation in aggregate output and inflation. These movements, in turn, can be related to variables associated with labor-market search theory, including unemployment benefits, labor productivity, real wages, and sectoral shifts in the labor ...
Working Paper
The importance of nonlinearity in reproducing business cycle features
This paper considers the ability of simulated data from linear and nonlinear time-series models to reproduce features in U.S. real GDP data related to business cycle phases. We focus our analysis on a number of linear ARIMA models and nonlinear Markov-switching models. To determine the timing of business cycle phases for the simulated data, we present a model-free algorithm that is more successful than previous methods at matching NBER dates and associated features in the postwar data. We find that both linear and Markov-switching models are able to reproduce business cycle features such as ...
Working Paper
A Bayesian approach to counterfactual analysis of structural change
In this paper, we develop a Bayesian approach to counterfactual analysis of structural change. Contrary to previous analysis based on classical point estimates, this approach provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. We apply the Bayesian counterfactual analysis to examine the sources of the volatility reduction in U.S. real GDP growth in the 1980s. Using Blanchard and Quah?s (1989) structural VAR model of output growth and the unemployment rate, we find strong statistical support for the idea that a counterfactual change in the ...
Working Paper
Inflation in the G7: mind the gap(s)?
We investigate the importance of trend inflation and the real-activity gap for explaining observed inflation variation in G7 countries since 1960. Our results are based on a bivariate unobserved-components model of inflation and unemployment in which inflation is decomposed into a stochastic trend and transitory component. As in recent implementations of the New Keynesian Phillips Curve, it is the transitory component of inflation, or ?inflation gap?, that is driven by the real-activity gap, which we measure as the deviation of unemployment from its natural rate. Even when allowing for ...
Working Paper
A steady-state approach to trend/cycle decomposition of regime-switching processes
In this paper, we present a new approach to trend/cycle decomposition under the assumption that the trend is the permanent component and the cycle is the transitory component of an integrated time series. The permanent component is defined as the steady-state level of the series, a definition that has exploitable forecasting implications useful for identification. We operationalize the steady-state approach for regime-switching processes and we use generated data from such processes to demonstrate the advantages of the steady-state approach over alternative approaches to trend/cycle ...