Identifying News Shocks from Forecasts

September 29, 2023

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Format: Chicago

Jonathan J. Adams, and Philip Barrett. Identifying News Shocks from Forecasts, (USA: International Monetary Fund, 2023) accessed December 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

We propose a method to identify the anticipated components of macroeconomic shocks in a structural VAR. We include empirical forecasts about each time series in the VAR. This introduces enough linear restrictions to identify each structural shock and to further decompose each one into “news” and “surprise” shocks. We estimate a VAR on US time series using forecast data from the SPF, CBO, Federal Reserve, and asset prices. Unanticipated fiscal stimulus and interest rate shocks we identify have typical effects that match existing evidence. In our news-surprise decomposition, we find that news drives around one quarter of US business cycle volatility. News explains a larger share of the variance due to fiscal shocks than for monetary policy shocks. Finally, we use the news structure of the shocks to estimate counterfactual policy rules, and compare the ability of fiscal and monetary policy to moderate output and inflation. We find that coordinated fiscal and monetary policy are substantially more effective than either tool is individually.

Subject: Econometric analysis, Expenditure, Fiscal policy, Fiscal stimulus, Inflation, Prices, Vector autoregression

Keywords: Coordinated fiscal policy, Data from the SPF, Fiscal multiplier, Fiscal policy, Fiscal stimulus, Global, Identification, Impulse response, Inflation, Monetary policy, Monetary policy shock, News, Policy counterfactuals, Structural shocks, SVAR, Using forecast data, Vector autoregression

Publication Details

Pages:

78
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2023/208
Stock No:

WPIEA2023208
ISBN:

9798400257377
ISSN:

1018-5941

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