While leading figures in the early history of economics conceived of it as inseparable from philosophy and other humanities, there has been movement, especially in recent decades, towards its becoming an essentially technical field with narrowly specialized areas of inquiry. Certainly, specialization has allowed for great progress in economic science. However, recent events surrounding the financial crisis support the arguments of some that economics needs to develop forums for interdisciplinary interaction and to aspire to broader vision.

A method is constructed for decomposing the variance of changes in incomes in the world into components, to indicate the most important risk-sharing opportunities among people of the world. A constant absolute risk premium (CARP) model, an intertemporal general-equilibrium model of the world, is presented to permit optimal contract design. For a contract designer maximizing a social welfare function, the optimal contracts maximize the equilibrium world real interest rate. Securities are defined in terms of eigenvectors of a transformed variance matrix. The method is applied using Penn World Table data on the G-7 countries, 1950–1992.

Tests of weak-form efficiency of the market for single family homes are performed using data on repeat sales prices of 39, 210 individual homes, each for two sales dates. Tests were done for Atlanta, Chicago, Dallas, and San Francisco/Oakland for 1970-86. While evidence for seasonality in real housing prices is weak, we do find some evidence of inertia in housing prices. A city-wide real log price index change in a given year tends to be followed by a city-wide real log price index change in the same direction (and between a quarter to a half as large in magnitude) in the subsequent year. However, the inertia cannot account for much of the variation in individual housing real price changes. There is so much noise in individual housing prices relative to city-wide price index changes that the R[squared] in forecasting regressions for annual real price change in individual homes is never more than .04.

Application of some advances in econometrics (in the theory of cointegrated vector autoregressive models) enables us to deal effectively with two problems in rational-expectations, present-valu e models: nonstationarity of time series and incomplete data on infor mation of market participants. With U.S. data, the authors find some relatively encouraging new results for the rational-expectations theo ry of the term structure and some puzzling results for the present-va lue model of stock prices.

This paper analyzes the correlation between interest rates and prices which as persisted for the past quarter of a millennium and has been termed the Gibson Paradox. Spectral techniques confirm the correlation between long-term interest rates and prices for very long-term swings (the Gibson Paradox), but indicate a significant short cycle correlation only for short-term interest rates, which we term the Kitchin Phenomenon. Past explanations of these correlations have often failed to distinguish cycle lengths and term of interest rates involved. Our analysis rejects Irving Fisher's "price expectation" explanation and the Sargent-Wicksell velocity of money explanations. We propose alternative explanations which in part relate to the characteristic behavior of governments during wartime and in part to distributional effects of unanticipated inflation. Our analysis strongly suggests that prior to World War I nominal long and short rates of interest can be regarded as real rates.

Robert J. Shiller, Fumiko Kon-Ya, Yoshiro Tsutsui, Why Did the Nikkei Crash? Expanding the Scope of Expectations Data Collection, The Review of Economics and Statistics, Vol. 78, No. 1 (Feb., 1996), pp. 156-164

The informational content of different forecasts can be compared by regressing the actual change in a variable to be forecasted on forecasts of the change. We use the procedure in Fair and Shiller (1987) to examine the informational content of three sets of ex ant. forecasts: the American Statistical Association and National Bureau of Economic Research Survey (ASA), Data Resources Incorporated (DRI), and Wharton Economic Forecasting Associates (UEFA). We compare these forecasts to each other and to quasi ex ante forecasts generated from a vector autoregressive model, an autoregressive components model, and a large-scale structural model (the Fair model).

The information contained in one model's forecast compared to that in another can be assessed from a regression of actual values on predicted values from the two models. The authors do this for forecasts of real GNP growth rates for different pairs of models. The models include a structural model (the Fair model), various versions of the vector autoregressive model, and various versions of a model the authors call the "autoregressive components" model. The authors' procedure requires that forecasts make no use of future information and they have been careful to try to insure this, including using the version of the Fair model that existed in 1976, the beginning of their test period.