Our answer: Not so well. We reached that conclusion after reviewing recent research on the role of technology as a source of economic fluctuations. The bulk of the evidence suggests a limited role for aggregate technology shocks, pointing instead to demand factors as the main force behind the strong positive comovement between output and labor input measures.

Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader's understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.

We use a model-based identification strategy to estimate the impact of technology shocks on hours worked and employment in the euro area. The sign restrictions applied in the vector autoregression (VAR) analysis are consistent with a large class of dynamic stochastic general equilibrium (DSGE) models and are robust to parameter uncertainty. The results are in line with the conventional Real Business Cycle (RBC) interpretation that hours worked rise as a result of a positive technology shock. By comparing the sign restrictions method to the long-run restriction approach of Galí (Quaterly Journal of Economics"(1992) 709-38), we show that the results do not depend on the stochastic specification of the hours worked series or the data sample but only on the identification scheme.