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Recent changes in the U.S. business cycle
The U.S. business cycle expansion that started in March 1991 is the longest on record. This paper uses statistical techniques to examine whether this expansion is a onetime unique event or whether its length is a result of a change in the stability of the U.S. economy. Bayesian methods are used to estimate a common factor model that allows for structural breaks in the dynamics of a wide range of macroeconomic variables. We find strong evidence that a reduction in volatility is common to the series examined. Further, the reduction in volatility implies that future expansions will be ...
Working Paper
Searching for Hysteresis
We search for the presence of hysteresis, which we dene as aggregate demand shocks that have a permanent impact on real GDP, in the U.S., the Euro Area, and the U.K. Working with cointegrated structural VARs, we nd essentially no evidence of such effects. Within a Classical statistical framework, it is virtually impossible to detect such shocks. Within a Bayesian context, the presence of these shocks can be mechanically imposed upon the data. However, unless a researcher is willing to impose the restriction that the sign of their long-run impact on GDP is the same for all draws, which amounts ...
Working Paper
Monetary Policy, Self-Fulfilling Expectations and the U.S. Business Cycle
I estimate a medium-scale New-Keynesian model and relax the conventional assumption that the central bank adopted an active monetary policy by pursuing inflation and output stability over the entire post-war period. Even after accounting for a rich structure, I find that monetary policy was passive prior to the Volcker disinflation. Sunspot shocks did not represent quantitatively relevant sources of volatility. By contrast, such passive interest rate policy accommodated fundamental productivity and cost shocks that de-anchored inflation expectations, propagated via self-fulfilling inflation ...
Working Paper
Financial Frictions, Financial Shocks, and Aggregate Volatility
The Great Moderation in the U.S. economy was accompanied by a widespread increase in the volatility of financial variables. We explore the sources of the divergent patterns in volatilities by estimating a model with time-varying financial rigidities subject to structural breaks in the size of the exogenous processes and two institutional characteristics: the coefficients in the monetary policy rule and the severity of the financial rigidity at the steady state. To do so, we generalize the estimation methodology developed by Curdia and Finocchiaro (2013). Institutional changes are key in ...
Working Paper
Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR
We use a mixed-frequency vector autoregression to obtain intraquarter point and density forecasts as new, high frequency information becomes available. This model, delineated in Ghysels (2016), is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. As this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. We obtain high-frequency updates to forecasts by treating new data releases as conditioning information. The same ...
Working Paper
Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR
We assess point and density forecasts from a mixed-frequency vector autoregression (VAR) to obtain intra-quarter forecasts of output growth as new information becomes available. The econometric model is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. We impose restrictions on the VAR to account explicitly for the temporal ordering of the data releases. Because this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. The ...
Working Paper
Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting
Financial data often contain information that is helpful for macroeconomic forecasting, while multistep forecast accuracy also benefits by incorporating good nowcasts of macroeconomic variables. This paper considers the role of nowcasts of financial variables in making conditional forecasts of real and nominal macroeconomic variables using standard quarterly Bayesian vector autoregressions (BVARs). For nowcasting the quarterly value of a variety of financial variables, we document that the average of the available daily data and a daily random walk forecast to fill in the missing days in the ...
Working Paper
Escaping the Great Recession
We show that policy uncertainty about how the rising public debt will be stabilized accounts for the lack of deflation in the US economy at the zero lower bound. We first estimate a Markov-switching VAR to highlight that a zero-lower-bound regime captures most of the comovements during the Great Recession: a deep recession, no deflation, and large fiscal imbalances. We then show that a micro-founded model that features policy uncertainty accounts for these stylized facts. Finally, we highlight that policy uncertainty arises at the zero lower bound because of a trade-off between mitigating the ...
Working Paper
A Generalized Approach to Indeterminacy in Linear Rational Expectations Models
We propose a novel approach to deal with the problem of indeterminacy in Linear Rational Expectations models. The method consists of augmenting the original state space with a set of auxiliary exogenous equations to provide the adequate number of explosive roots in presence of indeterminacy. The solution in this expanded state space, if it exists, is always determinate, and is identical to the indeterminate solution of the original model. The proposed approach accommodates determinacy and any degree of indeterminacy, and it can be implemented even when the boundaries of the determinacy region ...
Report
Time-Varying Structural Vector Autoregressions and Monetary Policy: a Corrigendum
This note corrects a mistake in the estimation algorithm of the time-varying structural vector autoregression model of Primiceri (2005) and shows how to correctly apply the procedure of Kim, Shephard, and Chib (1998) to the estimation of VAR, DSGE, factor, and unobserved components models with stochastic volatility. Relative to Primiceri (2005), the main difference in the new algorithm is the ordering of the various Markov Chain Monte Carlo steps, with each individual step remaining the same.