Modeling Multivariate Data Revisions

Jacobs, Jan; Sarferaz, Samad; Sturm, Jan‐Egbert; Norden, Simon van

doi:10.2139/ssrn.2370456

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Just as the model used in Jacobs & van Norden (2011), our model is general enough to allow for measurement errors that are correlated with either only final data ('news') or correlated only with the real-time data ('noise') as well as intermediate cases, as we show in the model section. Jacobs, Sarferaz, van Norden & Sturm (2013) build a multivariate version of Jacobs & van Norden (2011), but still abstract from stochastic volatility and time-varying parameters. Both Jacobs & van Norden (2011) and Jacobs et al (2013) do not study the response of the economy to structural shocks, which is our main focus.…”

Section: Introductionmentioning

confidence: 99%

“…Jacobs, Sarferaz, van Norden & Sturm (2013) build a multivariate version of Jacobs & van Norden (2011), but still abstract from stochastic volatility and time-varying parameters. Both Jacobs & van Norden (2011) and Jacobs et al (2013) do not study the response of the economy to structural shocks, which is our main focus. D'Agostino, Gambetti & Giannone (2013) use a VAR with time-varying parameters and stochastic volatility on real-time data to study the forecasting ability of models in this class.…”

Section: Introductionmentioning

confidence: 99%

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Measurement errors and monetary policy: Then and now

Amir-Ahmadi

Matthes

Wang

2017

Journal of Economic Dynamics and Control

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Should policymakers and applied macroeconomists worry about the difference between real-time and final data? We tackle this question by using a VAR with time-varying parameters and stochastic volatility to show that the distinction between real-time data and final data matters for the impact of monetary policy shocks: The impact on final data is substantially and systematically different (in particular, larger in magnitude for different measures of real activity) from the impact on real-time data. These differences have persisted over the last 40 years and should be taken into account when conducting or studying monetary policy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement errors and monetary policy: Then and now

Amir-Ahmadi

Matthes

Wang

2017

Journal of Economic Dynamics and Control

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“…3 Note that even after 20 quarters there are still a lot of revisions taking place in the growth rates. Apart from many large movements being caused by so-called major revisions, or comprehensive benchmarks (Jacobs et al, 2013), also many smaller adjustments are found for all three dates, even after a large amount of data releases have already occurred.…”

Section: Introductionmentioning

confidence: 97%

Modeling non-stationary and stationary mixed-frequency time series

Götz

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Combining distributions of real-time forecasts: An application to U.S. growth

Götz

Hecq

Urbain

2014

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Modeling Multivariate Data Revisions

Cited by 4 publications

References 38 publications

Measurement errors and monetary policy: Then and now

Measurement errors and monetary policy: Then and now

Modeling non-stationary and stationary mixed-frequency time series

Combining distributions of real-time forecasts: An application to U.S. growth

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