Information rigidities and the news-adjusted output gap

Garratt, Anthony; Lee, Kevin; Shields, Kalvinder

doi:10.1016/j.jedc.2016.06.004

Cited by 4 publications

(4 citation statements)

References 40 publications

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“…A challenge with such a univariate approach is that the interpretation of the estimated trend and cycle from a statistical filter often needs to be corroborated "off-model" with other sources of information. It is possible to directly allow for multivariate information to help conduct and interpret trend-cycle decompositions (e.g., Barigozzi & Luciani, 2018;Chan & Grant, 2017;Fleischman & Roberts, 2011;Garratt, Lee, & Shields, 2016;Garratt, Robertson, & Wright, 2006;Kozicki, 1999;Sinclair, 2009), but practical challenges remain in terms of determining exactly which variables should be included in the information set or even with how large the information set can be while still keeping estimation tractable.…”

Section: Introductionmentioning

confidence: 99%

Estimating and accounting for the output gap with large Bayesian vector autoregressions

Morley

Wong

2019

J of Applied Econometrics

121

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Summary We consider how to estimate the trend and cycle of a time series, such as real gross domestic product, given a large information set. Our approach makes use of the Beveridge–Nelson decomposition based on a vector autoregression, but with two practical considerations. First, we show how to determine which conditioning variables span the relevant information by directly accounting for the Beveridge–Nelson trend and cycle in terms of contributions from different forecast errors. Second, we employ Bayesian shrinkage to avoid overfitting in finite samples when estimating models that are large enough to include many possible sources of information. An empirical application with up to 138 variables covering various aspects of the US economy reveals that the unemployment rate, inflation, and, to a lesser extent, housing starts, aggregate consumption, stock prices, real money balances, and the federal funds rate contain relevant information beyond that in output growth for estimating the output gap, with estimates largely robust to substituting some of these variables or incorporating additional variables.

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Section: Introductionmentioning

confidence: 99%

Estimating and accounting for the output gap with large Bayesian vector autoregressions

Morley

Wong

2019

J of Applied Econometrics

121

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“…12. See Garratt, Lee, and Shields (2014) for a discussion of a measure of the natural output gap that is calculable in real time and that has an explicit economic motivation. See also Garratt et al (2008Garratt et al ( , 2009 for a more comprehensive discussion of the characterization of the output gap when there is uncertainty on how the concept is best measured.…”

Section: The Meta Taylor Rule and Model Averagingmentioning

confidence: 99%

“…See Garratt, Lee, and Shields () for a discussion of a measure of the natural output gap that is calculable in real time and that has an explicit economic motivation. See also Garratt et al.…”

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confidence: 99%

The Meta Taylor Rule

Lee¹,

Morley²,

Shields³

2015

J of Money Credit Banking

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We characterise U.S. monetary policy within a generalized Taylor rule framework that accommodates uncertainties about the duration of policy regimes and the specification of the rule, in addition to the standard parameter and stochastic uncertainties inher ent in traditional Taylor rule analysis. Our approach involves estimation and inference based on Taylor rules obtained through standard linear regression methods, but combined us ing Bayesian model averaging techniques. Employing data that were available i n real time, the estimated version of the 'meta' Taylor rule provides a flexible but compelling characterisation of monetary policy in the United States over the last forty years.

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“…One challenge with univariate detrending is that the interpretation of the estimated trend and cycle from a statistical filter often needs to be corroborated "off-model" with other sources of information. While one could allow an explicit role for multivariate information to help conduct and interpret trend-cycle decomposition (e.g., Kozicki, 1999;Garratt, Robertson, and Wright, 2006;Sinclair, 2009;Garratt, Lee, and Shields, 2016;Chan and Grant, 2017), practical challenges remain in terms of which variables should be included in the information set or even with how large the information set can be to keep estimation tractable.…”

Section: Introductionmentioning

confidence: 99%

Estimating and Accounting for the Output Gap with Large Bayesian Vector Autoregressions

Morley

Wong

2017

SSRN Journal

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We demonstrate how Bayesian shrinkage can address problems with utilizing large information sets to calculate trend and cycle via a multivariate Beveridge-Nelson (BN) decomposition. We illustrate our approach by estimating the U.S. output gap with large Bayesian vector autoregressions that include up to 138 variables. Because the BN trend and cycle are linear functions of historical forecast errors, we are also able to account for the estimated output gap in terms of different sources of information, as well as particular underlying structural shocks given identification restrictions. Our empirical analysis suggests that, in addition to output growth, the unemployment rate, CPI inflation, and, to a lesser extent, housing starts, consumption, stock prices, real M1, and the federal funds rate are important conditioning variables for estimating the U.S. output gap, with estimates largely robust to incorporating additional variables. Using standard identification restrictions, we find that the role of monetary policy shocks in driving the output gap is small, while oil price shocks explain about 10% of the variance over different horizons.

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Information rigidities and the news-adjusted output gap

Cited by 4 publications

References 40 publications

Estimating and accounting for the output gap with large Bayesian vector autoregressions

Estimating and accounting for the output gap with large Bayesian vector autoregressions

The Meta Taylor Rule

Estimating and Accounting for the Output Gap with Large Bayesian Vector Autoregressions

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