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VARs in 2020: outliers, the economic effects of uncertainty, and interest rates at the lower bound
The effects of COVID19 have generated remarkable movements in macroeconomic and financial data in 2020. Amongst others, these extreme observations have potentially strong effects on conventional VAR estimates, commonly used for macro forecasting. In addition, these swings raise important questions for measuring macroeconomic and financial uncertainty and their effects on the economy. Finally, short and longerterm interest rates are (back) at their effective lower bound (ELB). Together with Andrea Carriero, Massimiliano Marcellino, and Todd Clark, I am working on a few projects related to these questions. A set of slides summarizing some of our results can be found here. The working paper version of our work on measuring uncertainty and its effects in 2020 can be found here. 
Fully revised: Indeterminacy and Imperfect Information
with Thomas A. Lubik (FRB Richmond), Christian Matthes (FRB Richmond): We study equilibrium determination in an environment where two types of agents have different information sets: Fully informed agents observe histories of all exogenous and endogenous variables. Less informed agents observe only a strict subset of the full information set and need to solve a dynamic signal extraction problem to gather information about the variables they do not directly observe. Both types of agents know the structure of the model and form expectations rationally. In this environment, we identify a new channel that generates equilibrium indeterminacy: Optimal information processing of the less informed agent introduces stable dynamics into the equation system that lead to selffulling 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. 
Accepted at QE: StickyInformation Forecast paper with Jim Nason
Using stateoftheart particle filtering and smoothing, we show that inflation forecasts from the US SPF became “sticky” (more inattentive) only with the decline in inflation persistence that occurred after the Volcker disinflation. SPF predictions were much more attentive during the Great Inflation than now. The Figure below illustrates the importance of timevarying stickiness by comparing compares the MSE of actual SPF forecasts against the MSE of a hypothetical SPF that assumes they had been equally sticky during the Great Inflation as they are now. The MSE losses of those counterfactual forecasts would have been much higher. In contrast, while the stickiness of actual rose, their losses did not deteriorate much, as persistence in inflation declined (and the importance of noise shocks for inflation increased) at the same time. 
Replication codes for Johannsen and Mertens RealRate Trend Estimate
Replication files for my paper with Ben Johannsen on estimating the longerrun level of the real rate from a shadowrate model are now available on GitHub. The paper has also been accepted for publication by the Journal of Money, Credit, and Banking. For the working paper and more, please see here. Compared to other estimates known from the literature, our estimate attributes more of the recent declines in real rates to a cyclical decline, thus seeing a smaller decline in the trend rate. A key feature for this result is to embed stochastic volatility into the model specification. 
REVISED: Timevarying Forecast Stickiness w/Jim Nason
Finally, together with Jim Nason, we just finished a thorough revision of our paper on timevarying stickiness  i.e. a time/varying frequency of updating information  in professional forecasts. The paper confirms earlier findings (e.g. Coibion and Gorodnichenko, 2015 AER) that SPF predictions are "sticky" in that their forecast errors are predictable, but we also find evidence that stickiness was actually quite low during the Great Inflation of the 1970s and the ensuing Volcker disinflation. Stickiness rose only once inflation became much less persistent, so that updating to more recent information adds much less predictive content. The paper is here. See below for an abstract. Here is a picture of the estimated stickiness parameter, as well as confidence bands around changes in the stickiness parameter. Finally, here we show MSE loss of actual SPF forecasts compared against a counterfactual, where their stickiness is assumed to have been about as high as during the second half of our sample. ABSTRACT: Our paper studies the joint dynamics of U.S. inflation and a term structure of average inflation predictions taken from the Survey of Professional Forecasters (SPF). We combine an unobserved components (UC) model of inflation and a sticky information forecast mechanism to study these dynamics. The UC model decomposes inflation into a trend and a gap component and measurement error. We innovate by endowing inflation gap persistence and the frequency of sticky information inflation forecast updating with drift. Stochastic volatility is imposed on the innovations to trend and gap inflation. The result is a nonlinear state space model. The model is estimated on a sample from 1968:Q4 to 2018:Q3 using sequential Monte Carlo methods that include a particle learning filter and a RaoBlackwellized particle smoother. Our estimates show that (i) longer horizon average SPF inflation predictions inform estimates of trend inflation; (ii) inflation gap persistence is procyclical before the Volcker disinflation and acyclical afterwards; (iii) by 1990 sticky information inflation forecast updating is less frequent than it was earlier in the sample; and (iv) the drop in the frequency of the sticky information forecast updating occurs at the same time persistent shocks become less important for explaining fluctuations in inflation. All told, the data calls for drift in inflation gap persistence and in the frequency of updating sticky information forecasts. 
Accepted in REStat: Timevarying fan charts around survey forecasts
Forthcoming in the Review of Economics and Statistics , with Todd Clark (FRB Cleveland) and Michael McCracken (FRB St. Louis): We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track timevarying uncertainty in the associated forecast errors, we derive a multiplehorizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to simple variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. This is joint work with Todd Clark (FRB Cleveland) and Michael McCracken (FRB St. Louis) Here is a link to our earlier draft (Sep 2018): pdf And slides for a talk: pdf 
Accepted in JMCB: Shadow rates and the longrun level of the real rate
Together with my former Board colleague Ben Johannsen, we revised our work on estimating the longrun level of the real rate from a timeseries model with shadow rates. In the revised version we also estimate the effects of monetary policy shocks identified from shadowrate surprises
NEWLY REVISED working paper: pdf; accepted for publication by the JMCB. Slides for a talk are here: pdf Here are estimates of the trend level (and uncertainty around the trend estimates) as well as a modelimplied measure of the current real rate: Here we illustrate the construction of nonlinear impulseresponses from the actual rate (responses of macro variables can be found in the paper): Part of an earlier version of this work was also featured in the Board’s Monetary Policy Report to the Congress released on Feb 10, 2016, see pages 3233 of the report and a FEDS note. 
Kilian and Luetkepohl: Nice Section on Kurmann and Mertens (2014)
Kilian and Luetkepohl's SVAR monograph is out; a great book! A nice bit is also their description of my paper with Andre Kurmann (2014, AER) that showed how the original news shock identification of Beaudry and Portier (2006, AER; "BP") falls apart when there are more than two variables in their VECM. (The model has a common trend which limits the number of independent longrun restriction to just one.) Here is a figure from our paper that shows the range of impulse responses consistent with the BP newsshock identification: 
"Indeterminacy and Imperfect Information" with Thomas Lubik and Christian Matthes
This is ongoing work with Thomas A. Lubik (FRB Richmond), Christian Matthes (FRB Richmond): 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. We show that for parameters values that imply a unique equilibrium under full information, the limited information rational expectations equilibrium can be indeterminate. We illustrate our framework with a monetary policy problem where an imperfectly informed central bank follows an interest rate rule. 
REVISED: Paper on Timevarying Uncertainty in Survey Forecasts (w/Todd Clark and Michael McCracken)
We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track timevarying uncertainty in the associated forecast errors, we derive a multiplehorizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to simple variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. This is joint work with Todd Clark (FRB Cleveland) and Michael McCracken (FRB St. Louis) Here is a link to our current draft (Sep 2018): pdf And slides for a talk: pdf 
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