What does a technology shock do? A VAR analysis with model-based sign restrictions

Abstract

4Non-technical summary 56 Concluding remarks 33Abstract This paper estimates the effects of technology shocks in VAR models of the U.S., identified by imposing restrictions on the sign of impulse responses. These restrictions are consistent with the implications of a popular class of DSGE models, with both real and nominal frictions, and with sufficiently wide ranges for their parameterers. This identification strategy thus substitutes theoretically-motivated restrictions for the atheoretical assumptions on the time-series properties of the data that are key to long-run restrictions. Stochastic technology improvements persistently increase real wages, consumption, investment and output in the data; hours worked are very likely to increase, displaying a hump-shaped pattern. Contrary to most of the related VAR evidence, resultsare not sensitive to a number of specification assumptions, including those on the stationarity properties of variables. JEL classification: C3, E3An important task of macroeconomics is to develop models that account for specific, quantitative features of the business cycle. Modern business cycle theory envisions a central role of random fluctuations in technological progress in driving the bulk of aggregate fluctuations. When technology shocks as volatile and persistent as estimated total factor productivity (TFP) are fed through a standard real business cycle (RBC) model, the simulated economy appears to be able to replicate the patterns of volatilities and cross-correlations of key macroeconomic time series of the postwar U.S. economy. This is a remarkable result for alternative, demand-driven theories have a much harder time in generating key business cycle facts like the strong unconditional procyclicality of both labor productivity and hours worked.The notion that technology shocks have anything to do with business cycles, however, has been recently questioned by a growing literature that aims at testing the predictions of the theory in terms of conditional moments in the data, i.e. conditional on technology shocks being the source of fluctuations, rather than the moments analyzed by RBC models. The key difficulty is that technology shocks need to be identified in the data. The seminal contribution by Galí [1999] originally identified technology shocks with time-series methods as the only source of long-run movements in labor productivity.His results show that a positive technology shock induces a fall in hours worked so persistent that a negative conditional correlation between output and hours worked ensues. Initially the literature reached conclusions similar to Galí [1999]. As stressed by this author, not only does this evidence, taken at face value, reject a key prediction of standard RBC theory, but it highlights a feature of the economy's response to aggregate technology shocks whose relevance goes beyond any specific macroeconomic paradigm. Because of the procyclicality of hours worked, some other shock(s) rather than technology shocks must be driving observed aggregate fluctuat...

What are the Effects of Fiscal Policy Shocks?

“…Canova and Pappa (2006) also identify fiscal shocks using sign restrictions on impulse response correlations but control for monetary policy shocks by using data from economies (states and countries) belonging to monetary unions. More recently Dedola and Neri (2007) have used sign restrictions to identify technology shocks and Mountford (2005), Peersman (2005), Benati and Mumtaz (2007), Dungey and Fry (2007) and Fry and Pagan (2007) have addressed the identification of multiple shocks using sign restrictions.…”

The dynamic effects of shocks to wages and prices in the United States and the Euro Area

“…A unit root in labour productivity may also stem from permanent shocks to the e¢ ciency of investment (investment-speci…c technical change) according to Fisher (2006), or from shocks to the capital income tax (dividend taxation), according to Uhlig (2004). For a discussion on the interpretation and identi…cation of technology shocks, see for instance, Dedola and Neri (2006).…”

Re-reengineering the dream: agility as competitive adaptability

“…Whilst continuous change in technology and the associated effects of technology shock (Dedola and Neri, 2006;Christiano et al, 2003) are not new constructs, the reality of the industrial age was and is a continuing reduction in timeline for relevance and lifetime for a specific technology and the related skills and expertise base required for its effective implementation. This, combined with increasing pressures for innovation (Tidd and Bessant, 2013) and at times severe impacts from both local and global economic environments (Hitt et al, 2011) raises serious challenges for contemporary management teams seeking to strategically position a company and its technology base advantageously, relative to its suppliers, competitors and customers, as well as in predictive readiness for future technological change and opportunistic adaptation.…”

“…In considering how this 'Five-Influences Competitive Adaptability Analysis' model may be applied in the case of the European Union Factories of the Future collaborative Private-Public-Projects, it is clear that future implementation strategies should take cognisance of the strategic positioning and sociological aspects of contemporary manufacturing oriented organisations and their connection to the market context in which they are operating (Johnson et al, 2011). In particular, there is the need to appraise the potential impact of radical or disruptive new technology influences capable of inducing Kuhnian paradigmatic change (Kuhn, 1962;Turnbull, 1991) or Gestalt switch like shifts, or Constant's style technological revolution capable of inducing strategic discontinuity or 'technology shock' (Arnold, 2003;Constant, 1984;Tidd and Bessant, 2013) in an enterprise's knowledge, competencies, and underlying technology and applications base, regardless of the direction or source of such influence (Dedola and Neri, 2006;Christiano et al, 2003).…”