Structural vector autoregressions (SVARs) have become a standard tool used to determine the roles of monetary policy shocks in generating cyclical fluctuations in the United States. Using both long- and short-run identifying restrictions, various authors have explored the empirical response of the economy to exogenous monetary innovations. While the majority of the studies of monetary policy have focused on the effect of exogenous money growth or interest rate shocks, recent research has begun to investigate the effect of endogenous monetary policy -- that is, the central bank's reaction to non-monetary shocks. One exogenous shock that many economists believe contributes to the business cycle fluctuations that feed into the Taylor rule is the technology shock. In an effort to identify the empirical effects of technology shocks, Gali (1999) estimated two models: a bivariate model of productivity and hours and a five-variable model adding money, inflation, and interest rates. His identification estimates a decomposition of productivity and hours into innovations to technology and non-technology components by assuming that only the former can have long-run effects on labor productivity. Empirical identification of the technology shock was a key first step in developing a unified reduced-form framework with which to examine the role that monetary policy has played in smoothing economic fluctuations. Along these lines, Gali, Lopez-Salido, and Valles (2003 -- henceforth GLV) examined the endogenous response of monetary policy to identified technology shocks in the United States. GLV examine a four-variable structural VAR for the United States with labor productivity, labor hours, the real interest rate, and inflation. Using the Gali (1999) identification, they find that during the Volcker-Greenspan (VG) era the Fed's response to the technology shock is to raise the nominal interest rate, while during the Martin-Burns-Miller (MBM) era the Fed lowers the nominal rate. Moreover, they find that the inflation and hours responses in the two periods differ in sign. Our goal is to expand the scope of GLV to an international context to determine whether the effect of technology shocks is consistent across the major industrialized countries. In particular, we are interested in how the different central banks respond to technology shocks. We investigate the possibility that technology shocks in different countries produce fundamentally different inflation and employment responses and to what extent those effects alter the monetary response. Using a theoretical model adapted from King and Wolman (1996), we find that the empirical responses can be matched with theoretical responses. Differences in these theoretical responses can be attributed to alternative policy rules and changes in the cost of capital adjustment. Further tests verify that these country characteristics could, indeed, have some explanatory power. Our results are by no means conclusive; however, they do suggest a number of theoretically consistent similarities across countries in each subgroup. While we believe more investigation into these cross-country comparisons is warranted, the initial indication is that the manner in which monetary policy is conducted and the degree of rigidity in capital markets may be determining factors in a country's response to technology shocks. Gali, Jordi (1999). "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?" American Economic Review, March 1999, 89(1), pp. 249-271. Gali, Jordi; Lopez-Salido, J. David; and Valles, Javier (2003). "Technology Shocks and Monetary Policy: Assessing the Fed's Performance." Journal of Monetary Economics, May 2003, 50(4), pp. 723-743. King, Robert G., and Wolman, Alexander L. (1996). "Inflation Targetting in a St. Louis Model of the 21st Century." Federal Reserve Bank of St. Louis Review, May/June 1996, 78(3), pp. 83-107.
