I. INTRODUCTION In macroeconomics a standard way of measuring technology shocks is to use the Solow residual, Solow [1957]. However a growing body of evidence suggests the Solow residual is at best a noisy measure of technology shocks. A number of hypotheses have been advanced as to why this might be the case. These include: the existence of imperfect competition and increasing returns to scale (Hall [1988; 1990]), spillover externalities (Caballero and Lyons [1992]) and variations in factor utilization rates (Basu [1995], Burnside [1996], Burnside et at. [1993; 1995a,b], Burnside and Eichenbaum [1994] and Finn [1995]). Recent work by Basu [1995] and Burnside et al. [1995a], using data for United States (U.S.) manufacturing industries, suggests that changes in capital utilization are the most likely cause of the mis-measurement of technology by the Solow residual. This paper presents an econometric analysis of the behavior of the Solow residual for the Australian economy.(1) The preliminary analysis examines whether the Solow residual for Australia can be characterized as a strictly exogenous variable. Following Evans [1992], I test whether the Solow residual is Grangercaused by a number of other macroeconomic variables including: foreign prices and production, government spending, interest rates and a monetary aggregate. Fluctuations in such variables are frequently proposed as sources of business cycles in small open economies. Consistent with results obtained for the U.S. by Evans, I find that the Solow residual for Australia is not a strictly exogenous variable. However while Evans finds money supply growth and government spending Granger-cause the U.S. Solow residual, neither of these variables is a significant predictor in the Australian case. For Australia, the terms of trade and a measure of the term spread have the strongest predictive content for the Solow residual. What these results indicate is that the standard measure of the Solow residual for Australia is affected by shocks other than pure technological innovations. In an attempt to quantify the contribution of other (non-technology) shocks to fluctuations in the Solow residual for Australia, I employ the Structural Vector Autoregression (SVAR) methodology developed by Shapiro and Watson [1988], Blanchard and Quah [1989] and Cochrane [1994]. It is assumed that the measured Solow residual is affected by two types of mutually uncorrelated shocks. One of these has a permanent effect on the level of the Solow residual and the other has only a temporary effect. To identify these two disturbances I use information from a second variable, capacity utilization, where this choice is influenced by the recent work of Burnside and Eichenbaum [1994] and Burnside et al. [1995b]. Neither of the two shocks affecting the Solow residual has a permanent effect on the level of capacity utilization. The shock which has a permanent effect on the Solow residual is identified with a true technology disturbance, while the other shock is assumed to reflect aggregate demand disturbances. The remainder of this paper is organized as follows. In section II a measure of the Solow residual for Australia is constructed and its predictability examined. Section III contains a discussion of the SVAR model used to analyze the Solow residual and capacity utilization. The empirical results from this model are presented in section IV. Section V considers the robustness of the results and section VI concludes. II. PROPERTIES OF THE SOLOW RESIDUAL In macroeconomics a standard means of computing the (logarithm of the) Solow residual [a.sub.1] is to use the following equation, (1) [a.sub.t] = [y.sub.t] - [S.sub.N] [n.sub.t] - (1 - [S.sub.N])[k.sub.t] where y is the log of aggregate output, n is the log of total hours worked, k is the log of the aggregate capital stock and [S.sub.N] is labour's share of national income (assumed to be constant). …