Journal ArticleDOI
Factor Bias under Stochastic Technical Change
TLDR
In this paper, the influence of technological change in inducing factor bias in U.S. agricultural production between 1948 and 1983 was investigated using time-series procedures and a dynamic measurement error model to link research expenditures to the unobserved technological change variable.Abstract:
Time-series procedures are employed to determine the influence of technological change in inducing factor bias in U.S. agricultural production between 1948 and 1983. A dynamic measurement error model is used to link research expenditures to the unobserved technological change variable. Biasedness in labor and material factor shares is established.read more
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Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Journal ArticleDOI
Embodied and Disembodied Technical Change in Fisheries: An Analysis of the Sète Trawl Fishery, 1985–1999
TL;DR: In this article, the authors evaluated the impact of technical change on catch rates of 19 trawlers in the Sete trawl fleet of southern France, and found that embodied technical change enhanced productivity by approximately one percent per year between 1985 and 1999, but that external (disembodied) events counteracted this trend, causing a net output decline of about three percent.
Journal ArticleDOI
Aggregation without Separability: Tests of the United States and Mexican Agricultural Production Data
TL;DR: The generalized composite commodity theorem (GCCT) and testing procedure is extended to test for consistent aggregation of United States and Mexican agricultural production data in each category for which earlier tests rejected homothetic separability.
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Farm labor demand: a meta-regression analysis of wage elasticities.
Molly Espey,Dawn Thilmany +1 more
TL;DR: In this article, a meta-regression analysis of estimated demand wage elasticities was conducted to more clearly identify any systematic factors that influence such estimates, finding that the magnitudes of own-price demand elasticities are affected by differences including type and area of labor market, methodology, and time period covered by the data.
References
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ReportDOI
Endogenous Technological Change
TL;DR: In this paper, the authors show that the stock of human capital determines the rate of growth, that too little human capital is devoted to research in equilibrium, that integration into world markets will increase growth rates, and that having a large population is not sufficient to generate growth.
Posted Content
Endogenous Technological Change
TL;DR: In this paper, the authors show that the stock of human capital determines the rate of growth, that too little human capital is devoted to research in equilibrium, that integration into world markets will increase growth rates, and that having a large population is not sufficient to generate growth.
Book
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Journal ArticleDOI
Trends and random walks in macroeconmic time series: Some evidence and implications
TL;DR: In this paper, the authors investigate whether macroeconomic time series are better characterized as stationary fluctuations around a deterministic trend or as non-stationary processes that have no tendency to return to the deterministic path, and conclude that macroeconomic models that focus on monetary disturbances as a source of purely transitory fluctuations may not be successful in explaining a large fraction of output variation.
Posted Content
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.