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  • REVISED: Time-varying Forecast Stickiness w/Jim Nason Finally, together with Jim Nason, we just finished a thorough revision of our paper on time-varying stickiness --- i.e. a time/varying frequency of updating information -- in professional forecasts ...
    Posted Jun 4, 2019, 11:00 AM by Elmar Mertens
  • Accepted in REStat: Time-varying 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 ...
    Posted Feb 27, 2019, 5:10 AM by Elmar Mertens
  • ACCEPTED IN JMCB: Shadow rates and the long-run level of the real rate Together with my former Board colleague Ben Johannsen, we revised our work on estimating the long-run level of the real rate from a time-series model with shadow rates ...
    Posted Oct 21, 2019, 11:16 AM by Elmar Mertens
  • 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 ...
    Posted Mar 19, 2018, 11:24 PM by Elmar Mertens
  • "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 ...
    Posted Sep 7, 2019, 10:23 AM by Elmar Mertens
Showing posts 1 - 5 of 22. View more »

REVISED: Time-varying Forecast Stickiness w/Jim Nason

posted May 26, 2019, 7:07 AM by Elmar Mertens   [ updated Jun 4, 2019, 11:00 AM ]

Finally, together with Jim Nason, we just finished a thorough revision of our paper on time-varying 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 non-linear 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 Rao-Blackwellized 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: Time-varying fan charts around survey forecasts

posted Feb 27, 2019, 5:10 AM by Elmar Mertens

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 time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon 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 long-run level of the real rate

posted May 10, 2018, 12:02 AM by Elmar Mertens   [ updated Oct 21, 2019, 11:16 AM ]

Together with my former Board colleague Ben Johannsen, we revised our work on estimating the long-run level of the real rate from a time-series model with shadow rates. In the revised version we also estimate the effects of monetary policy shocks identified from shadow-rate surprises

 

NEWLY REVISED working paper: pdf.

Slides for a talk are here: pdf

Here are estimates of the trend level (and uncertainty around the trend estimates) as well as a model-implied measure of the current real rate:



Here we illustrate the construction of non-linear impulse-responses 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 32-33 of the report and a FEDS note.

   



Kilian and Luetkepohl: Nice Section on Kurmann and Mertens (2014)

posted Mar 19, 2018, 11:21 PM by Elmar Mertens   [ updated Mar 19, 2018, 11:24 PM ]

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 long-run restriction to just one.)

See here for more on our paper as well as Chapter 10 of the Kilian and Luetkepohl book.

Here is a figure from our paper that shows the range of impulse responses consistent with the BP news-shock identification:




"Indeterminacy and Imperfect Information" with Thomas Lubik and Christian Matthes

posted Jun 5, 2017, 11:53 AM by Elmar Mertens   [ updated Sep 7, 2019, 10:23 AM ]

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. 




Here is a slide describing our general setup:



Here is a range of equilibria obtained is a simpler Fisher-equation example (details are in the paper):


Here is a range of equilibria obtained from a richer NK model (details are in the paper):



REVISED: Paper on Time-varying Uncertainty in Survey Forecasts (w/Todd Clark and Michael McCracken)

posted Oct 13, 2016, 9:02 AM by Elmar Mertens   [ updated Sep 21, 2018, 1:49 AM ]

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon 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













FEDS Note on Shadow Rates and the Long-run Level of the Real Rate

posted Feb 10, 2016, 6:27 PM by Elmar Mertens   [ updated Jan 14, 2019, 7:44 AM ]

Together with my Board colleague Ben Johannsen, we just finished a FEDS note describing the latest version of our work on estimating shadow rates from time-series models. 


Part of this work is also featured in the Board’s Monetary Policy Report to the Congress that has just been released on Feb 10, 2016, see pages 32-33 of the report.

   


 

Since last summer we have made the model dynamics more general and are conditioning our estimates not only on macroeconomic aggregates and the federal funds rate, but also on a longer-term interest rate, which (unsurprisingly) provides a particularly useful signal about for inference about the path for short-term interest rates near the effective lower bound.

 

There is also a longer FEDS Working Paper, describing the underlying model, estimation method as well as additional results: pdf (FEDS),

and a NEWLY REVISED working paper: pdf (this site).

Slides for a talk are here: pdf

Here are slides for a shorter talk: pdf

New publications in JMCB, REStat, and IJCB

posted Aug 24, 2015, 9:49 AM by Elmar Mertens   [ updated May 19, 2016, 5:22 AM ]

A few of my papers recently got accepted for journal publication. Notably, "Managing Beliefs about Monetary Policy under Discretion" has just been published online in the Journal of Money, Credit, and Banking, "Measuring the Level and Uncertainty of Trend Inflation" has been accepted by the Review of Economics and Statistics, and "Trend Inflation in Advanced Economies" (joint with Christine Garnier and Edward Nelson) has recently been published in the September issue of the International Journal of Central Banking



Working paper versions of all papers can be found here: Working Papers

Time-varying Stickiness in Professional Inflation Forecasts

posted Mar 8, 2015, 7:20 PM by Elmar Mertens   [ updated Dec 29, 2017, 8:06 AM ]

In a new paper, co-authored with Jim Nason, we estimate a version of the Stock-Watson (SW) unobserved components (UC) model of inflation jointly with the Mankiw-Reis sticky information (SI) law of motion.


Jim  and I innovate on these models by adding time-varying persistence to the inflation gap of the SW-UC model in the form of a time-varying parameter AR(1). In the SI model we let the frequency of forecast updating be time-varying. These time-varying parameters (TVPs) are assumed to follow independent random walks. As is standard in the SW-UC model, the innovations to trend and gap inflation are afflicted with stochastic volatility (SV) that follow log random walks. 

The joint model is estimated on real time U.S. GNP/GDP inflation and the associated average inflation predictions of the Survey of Professional Forecasters (SPF) on a sample running from 1968Q4 to 2017Q2. We estimate the joint model using a particle filter algorithm. 


The joint model with time-varying inflation gap persistence also produces less sticky average SPF inflation predictions than with a fixed coefficient AR(1) inflation gap. We also find the SV of trend inflation exhibits negative comovement with the time-varying frequency of SI forecast updating while the SV and time-varying persistence of gap inflation often show positive comovement. Thus, the average SPF respondent is most sensitive to the impact of permanent shocks on the conditional mean of inflation.


Finally, here are the estimates of our stickiness parameter (filtered in black, smoothed in red), 



and confidence intervals about the change in stickiness since the beginning of our sample:







The paper is here:  pdf

Slides are here: pdf (This is an updated version of our talk at the NBER SI) 


On the Reliability of Output Gap Revisions

posted Feb 22, 2014, 6:48 PM by Elmar Mertens   [ updated Nov 15, 2014, 5:14 AM ]


Over the last year I have been working on a new working paper, which considers revisions in output gap estimates derived from statistical trend-cycle decompositions, just as Orphanides and van Norden (2002, ReStat) did. However, I consider models with stochastic volatility, that can adapt to the changing patterns in aggregate volatility like before (and after) the Great Moderation period. The model with time-varying volatility generate credible sets for the output gap that are tighter than in the constant-paramter case. Also, when comparing realtime estimates against "final" estimates (derived from the latest available data vintage) revisions are quite a bit smaller. 

Slides for a talk can be found here (or here)

A draft of the paper is here.









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