Explaining the Labor Force Participation of Women 20-24

Between about the mid 1960s and the late 1970s there was a remarkable rise in the labor force participation of women and then a leveling off that has persisted through the mid 1990s. This paper attempts to explain the labor force participation of women 20-24 over this period. A "relative income" variable is constructed based on Easterlin's (1980) relative income hypothesis, and this is found to be an important explanatory variable. Easterlin's "cohort wage" hypothesis is also used in the analysis. The basic equation estimated does very well in various tests that were performed on it, and it appears to explain well the rapid rise and then leveling off of the labor force participation of young women.

The Informational Content of Ex Ante Forecasts

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).

Econometric Modeling as Information Aggregation

A forecast produced by an econometric model is a weighted aggregate of predetermined variables in the model. In many models the number of predetermined variables used is very large, often exceeding the number of observations. A method is proposed in this paper for testing an econometric model as an aggregator of the information in these predetermined variables relative to a specified subset of them. The test, called the "information aggregation" (IA) test, tests whether the model makes effective use of the information in the predetermined variables or whether a smaller information set carries as much information. The method can also be used to test one model against another. The method is used to test the Fair model as an information aggregator. The Fair model is also tested against two relatively non theoretical models: a VAR model and an "autoregressive components" (AC) model. The AC model, which is new in this paper, estimates an autoregressive equation for each component of real GNP, with real GNP being identically determined as the sum of the components. The results show that the AC model dominates the VAR model, although both models are dominated by the Fair model. The results also show that the Fair model seems to be a good information aggregator.

Numerical integration of nonlinear differential equations by use of rational approximation Final report, 15 Jan. 1965 - 14 Jul. 1967

Numerical integration of nonlinear differential equations by use of rational approximatio

Fluid model for a network operating under a fair bandwidth-sharing policy

We consider a model of Internet congestion control that represents the randomly varying number of flows present in a network where bandwidth is shared fairly between document transfers. We study critical fluid models obtained as formal limits under law of large numbers scalings when the average load on at least one resource is equal to its capacity. We establish convergence to equilibria for fluid models and identify the invariant manifold. The form of the invariant manifold gives insight into the phenomenon of entrainment whereby congestion at some resources may prevent other resources from working at their full capacity

Winter cereal pasture and Eragrostis culvula hay for fat lamb production

No Abstract

The Informational Content of Ex Ante Forecasts

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 ante forecasts: the American Statistical Association and National Bureau of Economic Research Survey (ASA). Data Resources Incorporated (DRI), and Wharton Economic Forecasting Associates (WEFA). 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)

Econometric Modeling as Information Aggregation

The information contained in the forecasts from two econometric models can be compared by regressing the actual change in the variable forecasted on the two forecasts of the change. We do such comparisons in this paper, where the forecasts are based only on information through the period prior to the first period of the forecast. If a model’s forecast is statistically significant in such a regression, we conclude that the model captures information not in the other model whose forecast is also included in the regression. The models studied include the Fair model, vector autoregressive (VAR) models estimated by ordinary least squares, vector autoregressive models estimated with Litterman priors, and a new class of models, which we call “autoregressive components: (AC) models. The AC models divide GNP into components and estimate an autoregressive equation for each component. Our results show that the Fair model’s forecasts contain information not in the forecasts of the VAR and AC models. The AC models contain no information not in the Fair model, which indicates that the Fair model uses all the useful information in the components. The VAR models contain information not in the Fair model for the four-quarter-ahead forecasts but not the one-quarter-ahead forecasts. The best AC model contains information not in the best VAR model, which indicates that there is useful information in the components that the VAR models are not using. The best VAR model contains information not in the best AC model for the four-quarter-ahead forecasts but not the one-quarter-ahead forecasts

Explaining the Labor Force Participation of Women 20-24

Between about the mid 1960s and the late 1970s there was a remarkable rise in the labor force participation of women and then a leveling off that has persisted through the mid 1990s. This paper attempts to explain the labor force participation of women 20-24 over this period. A “relative income” variable is constructed based on Easterlin’s (1980) relative income hypothesis, and this is found to be an important explanatory variable. Easterlin’s “cohort wage” hypothesis is also used in the analysis. The basic equation estimated does very well in various tests that were performed on it, and it appears to explain well the rapid rise and then leveling off of the labor force participation of young women