Revisions in Utilization-Adjusted TFP and Robust Identification of News Shocks

Kurmann, André; Sims, Eric R.

doi:10.1162/rest_a_00896

Cited by 46 publications

(37 citation statements)

References 52 publications

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“…Interestingly, the e¤ect of using the instrumented Macroeconomist's VAR is observed even for short horizons when evaluating responses for TFP. The e¤ects of TFP measurement errors on estimates of the transmission of shocks using VAR models has been studied by Cascaldi-Garcia (2017) and Kurmann and Sims (2017), amongst others.…”

Section: Resultsmentioning

confidence: 99%

Measuring the effects of expectations shocks

Padgett

Galvão

2021

Journal of Economic Dynamics and Control

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We show that expectation shocks -revisions in expectations unrelated to changes in current economic fundamentals -have positive signi…cant e¤ects on US economic activity. To measure the expectation shocks, we estimate a mixed-frequency VAR model that allows economic conditions in the current quarter to a¤ect current-quarter GDP expectations. The expectations shock is estimated with real-time data so such shocks do not su¤er a 'look-forward'bias by incorporating future data revisions. Dynamic responses are estimated with the aid of a quarterly VAR and using older vintages as instruments to account for measurement errors in the observed values.Expectations shocks explain 10% of the two-year variation of output, investment, consumption and hours. We …nd that expectations shocks are correlated with alternative belief-based shocks, but nevertheless have signi…cant additional short-run e¤ects.

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Section: Resultsmentioning

confidence: 99%

Measuring the effects of expectations shocks

Padgett

Galvão

2021

Journal of Economic Dynamics and Control

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“…The literature on news shocks often highlights that news shocks explain a significant part of the FEV in macro variables (e.g., Beaudry and Portier (2014)). Barsky and Sims (2011), and Kurmann and Sims (2017) advocate identifying productivity news shocks by maximizing the FEV. Here, we decompose the FEV using both prior‐robust and conditionally uniform beliefs.…”

Section: Application: Productivity News Shocksmentioning

confidence: 99%

“…We demonstrate empirically that ranking restrictions can meaningfully sharpen inference by analyzing productivity news shocks. Beaudry and Portier (2006) and, more recently, Kurmann and Sims (2017) have argued that productivity news shocks are an important determinant of output fluctuations. In the literature, TFP news shocks raise the value of stocks, are expansionary, and lead to higher TFP in the future.…”

Section: Introductionmentioning

confidence: 99%

Identification and inference with ranking restrictions

Amir-Ahmadi

Drautzburg

2021

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We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

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“…The literature on news shocks often highlights that news shocks explain a significant part of the FEV in macro variables (e.g., Beaudry and Portier, 2014). Barsky andSims (2011), andKurmann andSims (2017) advocate identifying productivity news shocks by maximizing the FEV. Here, we decompose the FEV using both prior-robust and conditionally uniform beliefs.…”

Section: Forecast Error Variance Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The discretization filter: A simple way to estimate nonlinear state space models

Farmer

2021

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Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of nonlinear, non‐Gaussian state space models. I establish that the associated maximum likelihood estimator is strongly consistent, asymptotically normal, and asymptotically efficient. Through simulations, I show that the discretization filter is orders of magnitude faster than alternative nonlinear techniques for the same level of approximation error in low‐dimensional settings and I provide practical guidelines for applied researchers. It is my hope that the method's simplicity will make the quantitative study of nonlinear models easier for and more accessible to applied researchers. I apply my approach to estimate a New Keynesian model with a zero lower bound on the nominal interest rate. After accounting for the zero lower bound, I find that the slope of the Phillips Curve is 0.076, which is less than 1/3 of typical estimates from linearized models. This suggests a strong decoupling of inflation from the output gap and larger real effects of unanticipated changes in interest rates in post Great Recession.

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Revisions in Utilization-Adjusted TFP and Robust Identification of News Shocks

Cited by 46 publications

References 52 publications

Measuring the effects of expectations shocks

Measuring the effects of expectations shocks

Identification and inference with ranking restrictions

The discretization filter: A simple way to estimate nonlinear state space models

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