Working Papers

My research is also posted at IDEAS, SSRN, GoogleScholar, ResearcherID, ORCID.

Codes can also be found at GitHub. Below you also find GitHub links to individual projects.

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

with Andrea Carriero (Queen Mary University of London), Todd Clark (Federal Reserve Bank of Cleveland), Massimiliano Marcellino (Bocconi, IGIER and CEPR)

  • draft: pdf (revised August 2021)

  • supplementary results: pdf


Abstract: The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that outlier-augmented SV schemes do at least as well as a conventional SV model. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the period since the pandemic's outbreak, as well as for earlier subsamples of relatively high volatility.

Forecasting with Shadow-Rate VARs

with Andrea Carriero (Queen Mary University of London), Todd Clark (Federal Reserve Bank of Cleveland), Massimiliano Marcellino (Bocconi, IGIER and CEPR)

Abstract: Interest rate data are an important element of macroeconomic forecasting. Not only are projections of future interest rates an object of interest themselves, but also they matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear vector autoregressions (VARs) that include shorter- and longer-term interest rates. However, in a number of economies, shorter-term interest rates have by now been stuck at or near their effective lower bound (ELB) for years, with longer-term rates drifting toward the constraint as well. In such an environment, linear forecasting models that ignore the ELB constraint on nominal interest rates can be problematic along various dimensions. Instead, we model nominal interest rates as censored observations of a latent shadow-rate process and relate their dynamics to other economic variables in a so-called ``shadow-rate VAR.'' We consider specifications where both actual and shadow interest rates, or only shadow interest rates, matter for forecasting. Our empirical application studies the performance of point and density forecasts generated by shadow-rate VARs for US data since 2009. In comparison to a standard VAR, shadow-rate VARs generate superior predictions for short- and long-term interest rates. By ignoring the ELB, the standard VAR can generate a negative-rate outlook which influences its economic forecasts in ways that can be similar to a shadow-rate VAR. On balance, across measures of economic activity and inflation, the accuracy of forecasts from our shadow-rate specifications is on par with a standard VAR.


Measuring Uncertainty and Its Effects in the COVID-19 Era

with Andrea Carriero (Queen Mary University of London), Todd Clark (Federal Reserve Bank of Cleveland), Massimiliano Marcellino (Bocconi, IGIER and CEPR)



Abstract:


We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty, in addition to idiosyncratic components. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. We also consider an extended version of the model, based on a latent state approach to accommodating outliers in volatility, to reduce the influence of extreme observations from the COVID period. The estimates we obtain yield very large increases in macroeconomic and financial uncertainty over the course of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but with both models, the contributions of uncertainty are small compared to the overall movements in many macroeconomic and financial indicators. That implies that the downturn is driven more by other dimensions of the COVID crisis than shocks to aggregate uncertainty (as measured by our method).


Indeterminacy and Imperfect Information

with Thomas A. Lubik and Christian Matthes

(Revised November 2019)

  • draft: pdf

  • supplementary appendix: pdf

Abstract: We study equilibrium determination in an environment where two kinds of agents have different information sets: The fully informed agents know the structure of the model and observe histories of all exogenous and endogenous variables. The less informed agents observe only a strict subset of the full information set. All types of agents form expectations rationally, but agents with limited information need to solve a dynamic signal extraction problem to gather information about the variables they do not observe. In this environment, we identify a new channel that leads to equilibrium indeterminacy: Optimal information processing of the less informed agent introduces stable dynamics into the equation system that lead to self-fulling expectations. For parameter values that imply a unique equilibrium under full information, the limited information rational expectations equilibrium is indeterminate. We illustrate our framework with a monetary policy problem where an imperfectly informed central bank follows an interest rate rule.

Older working papers

On the Reliability of Output Gap Estimates in Real Time

  • slides (pdf, 2019)

  • draft (pdf, 2014)

Abstract: Real-time estimates of the Output Gap — defined as the cyclical component of GDP — have previously been shown to be unreliable, since they are subject to large revisions when new data comes in. However, this result has so far only been derived for constant parameter models. This paper uses statistical models where the volatility of shocks to trend and cycle can vary over time. In this case, output gap estimates derived from data vintages going back to the 1970s are much closer to “final” estimates derived from all available sample data. The final estimates not only fall mostly within the credible intervals generated by the real-time data. When generated from a model with stochastic volatility, these credible sets are also tighter, at least over low-volatility periods.


Discreet Commitments and Discretion of Policymakers with Private Information

Abstract:

This papers presents general methods to compute optimal commitment and discretion policies, when a policymaker is better informed about the realization of some shocks than the public. In this situation, public beliefs about the hidden information emerge as additional state variables, managed by the policymaker.

Under commitment, policy is additive in two components: The optimal policy, as if the government shared the public's information set and the systematic manipulation of that information set. Even under discretion, belief management imparts history dependence.

Illustrated in a New Keynesian economy with time-varying output targets of the policymaker, belief management improves outcomes compared to symmetric information. At the margin, the policymaker tries to be intransparent about policy objectives by engineering disturbances which lower public beliefs about the persistence of output targets.


Correct variance for estimated Sharpe Ratios