This book provides a detailed analysis of U.S. economic transformation in the last 50 years, including the principal drivers for economic growth, U.S. demographic transformation, and the changing sector structure of the U.S. economy. Economic Transformation of the United States, 1950-2000, provides contemporary and historical contexts to illustrate how technological innovation and the changing American ideology play a role in the process of U.S. economic transformation. It describes the services sector in which one set of service industries is indentified as wealth providers and another set as job providers.

In aggregation theory, index numbers are judged relative to their ability to track the exact aggregator functions nested within the economy's structure. We compare two statistical index numbers—the Divisia monetary aggregate and the simple-sum monetary aggregate—with the exact rational expectations monetary aggregate, using actual data. Because we are not using simulated data, we estimate the parameters of the Euler equations, and thereby of the nested monetary aggregator function, using the generalized method of moments. We explore the tracking errors of the two index numbers relative to the estimated exact aggregate. We investigate the circumstances under which risk aversion increases tracking error. We also use polyspectral methods to test for the existence of remaining nonlinear structure in the residual tracking errors.

Preface: Facing the Challenge of Global Competition. 1. An Overview of International Economic Competition. 2. The Changing Economic Conditions of the World. 3. International Trade Patterns in the Global Electronics Industry. 4. International Competition: Global Electronics Companies. 5. The Core Competition of Global Giant Electronics Companies. Appendix: A List of the Sample Companies (316 in Total). References. Index.

This paper provides an approach to the estimation of technology parameters in the financial sector. The relevant technologies are those of the financial intermediaries that produce inside money as output services and the nonfinancial firms that demand financial services as inputs to production technology. Virtually every firm in the economy falls into one of those two groups. We also provide the analogous results for consumer demand. In that case, the Euler equation parameters become parameters of tastes, rather then of technolgy. The problems that we seek to solve through our approach to modeling and estimation of those tastes and technologies are the "Lucas Critique" and what Chrystal and MacDonald (1994, p. 76) recently have called the "Barnett Critique." We also explore the tracking ability of the Divisia monetary aggregates and simple sum monetary aggregates relative to the GMM estimated exact rational expectations monetary aggregates nested within the technologies of firms and utility functions of consumers.

Piyu Yue, a research associate at the IC2 Institute, University of Texas at Austin, was a visiting scholar at the Federal Reserve Bankof St. Louis when this article was written. Lynn Dietrich providedresearch assistance. The author would like to thank A. Charnes, Roll Fare and Shawna Grosskopf for theirconstructive comments and useful suggestions. Their DEA computer code led to a significant improvement ofthe paper

Daniel L. Thornton and Piyu Yue discuss monetary aggregation and present an extended data series of Divisia monetary aggregates. They briefly analyze the behavior of five Divisia aggregates—M1A, M1, M2, M3, and L. Their analysis suggests that the method of aggregation is not likely to be empirically important for narrow monetary aggregates like M1A and M1 and that beyond some point, successively broader Divisia monetary aggregates are likely to behave similarly in applied work.

See Friedman (1956) for one of the most comprehensive discussions of the money demand function.2 These functions have been subject to several unexplainable shifts and often imply a larger liquidity effect than is typically experienced.Perhaps the most dramatic example of this phenomenon occurred in the early l9BOs with the yet unexplained break in the income velocity of Ml.For this and other examples, see Goldfeld (1976), Friedman (1984), Lucas (19BB) and Rasche (1990).3 Frequently, the estimated own price elasticities of demand for monetary assets are positive, implying that their demand curves slope upward.For example, see Serletis (1988), Fisher (1989) and Moore, Porter and Small (1990).FFflFRAI PFSFRVF RANK OF SiT LOUIS 1 37 'Mankiw (1990), page 1658.'Some economists argue that aggregate data cannot be applied to microeconomic models without considering the problems of aggregation.Aggregation problems are not discussed in this paper, although the aggregation error might be one source of the unsatisfactory performance of conventional money-demand functions.'The user cost of holding a unit of a real monetary asset is computed by the formula, u p'(t) [R(t) -i(t)Jlll + R(t)], where p'(t) is the "true" cost of living index defined as the geometric average of the consumer price index and the consumption goods deflator, R(t) is the benchmark interest rate or the maximum rate in the economy at each period and i(t) is the interest rate on the monetary asset, The formula is derived from a widely applicable consumer decision model.'Distinct views about money have resulted in two approaches to analyzing consumer demand for money.In the first approach, money is viewed as a commodity which provides a monetary service flow to holders.Thus, real balances of the monetary assets directly enter the con-' 1 See Fisher (1989), pp.

Piyu Yue and Robert Fluri derive alternative monetary aggregates for Switzerland that are based on economic aggregation theory, Divisia M1 and Divisia M2. Noting that the historical relationship between the Swiss National Bank’s principal policy instrument, the monetary base, and inflation appears to have broken down in the 1980s, they compare the performance of their two new aggregates and simple-sum M1 and M2 in explaining Swiss inflation.