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Unraveling News: Reconciling Conflicting Evidence

  • Maria Bolboaca EMAIL logo and Sarah Fischer

Abstract

This paper addresses the lack of consensus in the empirical literature regarding the effects of technology diffusion news shocks. We attribute the conflicting evidence to the wide diversity in terms of variable settings, productivity series used, and identification schemes applied. We analyze the different identification schemes that have been employed in this literature. More specifically, we impose short- and medium-run restrictions to identify a news shock. The focus is on the medium-run identification maximizing at and over different horizons. We show that the identified news shock depends critically on the applied identification scheme and on the maximization horizon. We also investigate the importance of the information content of the model and of the productivity measure used. We find that models which either contain a large set of macroeconomic variables or include variables that are strongly forward looking deliver more robust results. Moreover, we show that the productivity series used may influence results, but there is convergence of findings for newer total factor productivity series vintages. Our conclusion is that news shocks have expansionary properties.

JEL Classification: E23; E32

Corresponding author: Maria Bolboaca, Study Center Gerzensee, Dorfstrasse 2, 3115Gerzensee, Switzerland; and Department of Economics, University of Bern, Schanzeneckstrasse 1, 3001Bern, Switzerland, E-mail:
This paper supersedes a prior paper circulated under the title “Anticipated and unanticipated productivity shocks.” We are thankful for the insightful comments of Fabrice Collard, Harris Dellas, Patrick Fève, Sylvia Kaufmann, Klaus Neusser, Franck Portier, Mark Watson, and of conference and seminar participants at the Econometric Society European Winter Meeting (Rotterdam), 21st SMYE (Lisbon), Study Center Gerzensee, and University of Bern. We assume responsibility for all remaining errors.Present address: Maria Bolboaca, Institute of Economics, University of St. Gallen, Varnbüelstrasse 19, 9000 St. Gallen, Switzerland.Sarah Fischer, State Secretariat for Economic Affairs SECO, Holzikofenweg 36, 3003 Bern, Switzerland.

Funding source: IMG Stiftung

Award Identifier / Grant number: 35/16 and 36/16

Appendices

A: Data

TFP: log tfp adj. for capacity utilization (from Federal Reverse Bank of San Francisco, following the method of Basu et al. (2013); Basu, Fernald, and Kimball (2006); Fernald (2014).

cc: index of consumer sentiment (US CONSUMER CONFIDENCE – EXPECTATIONS SADJ/US UNIVERSITY OF MICHIGAN: CONSUMER EXPECTATIONS VOLN, USCCONFEE, M, extracted from Datastream).

Y: log real per capita output nonfarm (log of Real gross value added: GDP: Business: Nonfarm, A358RX1Q020SBEA, Q, sa, US Department of Commerce: Bureau of Economic Analysis; adjusted for population: US POPULATION, WORKING AGE, ALL PERSONS (AGES 15–64) VOLN, USMLFT32P, M, retrieved from Datastream).

Infl: inflation rate (4*log-difference of Nonfarm Business Sector: Implicit Price Deflator, IPDNBS, Q, sa, US Department of Labor: Bureau of Labor Statistics).

SP: log real per capita stock stock prices (log of S&P 500, http://data.okfn.org/data/core//s-and-p-500#data; divided by the price deflator and population).

C: log real per capita consumption (log of Personal Consumption Expenditures: Nondurable Goods, PCND, Q, sa, US Department of Commerce: Bureau of Economic Analysis + Personal Consumption Expenditures: Services, PCESV, Q, sa, US Department of Commerce: Bureau of Economic Analysis; divided by the price deflator and population).

I: log real per capita investment (log of Personal Consumption Expenditures: Durable Goods, PCDG, Q, sa, US Department of Commerce: Bureau of Economic Analysis + Gross Private Domestic Investment, GPDI, Q, sa, US Department of Commerce: Bureau of Economic Analysis; divided by the price deflator and population).

H: log per capita hours (log Nonfarm Business Sector: Hours of All Persons, HOANBS, Q, sa, US Department of Labor: Bureau of Labor Statistics; divided by population).

i: nominal interest rate (Effective Federal Funds Rate, FEDFUNDS, M (averages of daily figures), nsa, Board of Governors of the Federal Reserve System).

Solow residual: (log(tfp)=log(Y/(H(av(ls))KS(1av(ls)); ls:Share of Labour Compensation in GDP at Current National Prices for United States, LABSHPUSA156NRUG, annual, nsa, University of Groningen, University of California, Davis; KS: US CBO FCST SURVEY-INDEX OF CAPITAL SERVICES(NONFARM BUS SECT), USFCICSN, annual/linearly interpolated, US CBO.

B: Model Settings

C: Cross-Correlations Between Shocks Obtained in Various Settings

D: Cross-Correlations Between Shocks from Settings Used in the Literature

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Received: 2019-10-09
Accepted: 2021-01-06
Published Online: 2021-02-19

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