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.
Funding source: IMG Stiftung
Award Identifier / Grant number: 35/16 and 36/16
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:
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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