Identifying news shocks with forecast data

Yasuo Hirose; Takushi Kurozumi

doi:10.1017/S1365100519000737

Identifying news shocks with forecast data

Yasuo Hirose, Takushi Kurozumi

Faculty of Economics

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The empirical importance of news shocks - anticipated future shocks - in business cycle fluctuations has been explored by using only actual data when estimating models augmented with news shocks. This paper additionally exploits forecast data to identify news shocks in a canonical dynamic stochastic general equilibrium model. The estimated model shows new empirical evidence that technology news shocks are a major source of fluctuations in US output growth. Exploiting the forecast data not only generates more precise estimates of news shocks and other parameters in the model, but also increases the contribution of technology news shocks to the fluctuations.

Original language	English
Pages (from-to)	1442-1471
Number of pages	30
Journal	Macroeconomic Dynamics
Volume	25
Issue number	6
DOIs	https://doi.org/10.1017/S1365100519000737
Publication status	Published - 2021 Sept 10

Keywords

Bayesian Estimation
Business Cycle Fluctuation
Forecast Data
Technology News Shock
Technology Shock

ASJC Scopus subject areas

Economics and Econometrics

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10.1017/S1365100519000737

Cite this

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title = "Identifying news shocks with forecast data",

abstract = "The empirical importance of news shocks - anticipated future shocks - in business cycle fluctuations has been explored by using only actual data when estimating models augmented with news shocks. This paper additionally exploits forecast data to identify news shocks in a canonical dynamic stochastic general equilibrium model. The estimated model shows new empirical evidence that technology news shocks are a major source of fluctuations in US output growth. Exploiting the forecast data not only generates more precise estimates of news shocks and other parameters in the model, but also increases the contribution of technology news shocks to the fluctuations.",

keywords = "Bayesian Estimation, Business Cycle Fluctuation, Forecast Data, Technology News Shock, Technology Shock",

author = "Yasuo Hirose and Takushi Kurozumi",

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N2 - The empirical importance of news shocks - anticipated future shocks - in business cycle fluctuations has been explored by using only actual data when estimating models augmented with news shocks. This paper additionally exploits forecast data to identify news shocks in a canonical dynamic stochastic general equilibrium model. The estimated model shows new empirical evidence that technology news shocks are a major source of fluctuations in US output growth. Exploiting the forecast data not only generates more precise estimates of news shocks and other parameters in the model, but also increases the contribution of technology news shocks to the fluctuations.

AB - The empirical importance of news shocks - anticipated future shocks - in business cycle fluctuations has been explored by using only actual data when estimating models augmented with news shocks. This paper additionally exploits forecast data to identify news shocks in a canonical dynamic stochastic general equilibrium model. The estimated model shows new empirical evidence that technology news shocks are a major source of fluctuations in US output growth. Exploiting the forecast data not only generates more precise estimates of news shocks and other parameters in the model, but also increases the contribution of technology news shocks to the fluctuations.

KW - Bayesian Estimation

KW - Business Cycle Fluctuation

KW - Forecast Data

KW - Technology News Shock

KW - Technology Shock

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Identifying news shocks with forecast data

Abstract

Keywords

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