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| LaFrance, Jeffrey T.; Burt, Oscar R.. |
| Aggregate U.S. agricultural supply response is modeled through a modified partial adjustment model, where the effects of weather and other temporal stochastic effects are structured to be purely static, while the effects of price and technology, or trend, are dynamic. The model is applied to a time series of aggregate U.S. farm output, aggregate U.S. crop production, and aggregate U.S. livestock and livestock products production for several sample periods within the period 1911-1958. The three aggregate output indexes are tested for irreversibilities in supply response, and no evidence of a definitive irreversible supply function is found for any of the dynamic supply models. The use of a nonstochastic difference equation to model the aggregate farm... |
| Tipo: Journal Article |
Palavras-chave: Demand and Price Analysis; Production Economics. |
| Ano: 1983 |
URL: http://purl.umn.edu/32483 |
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| Koo, Won W.; Burt, Oscar R.. |
| A method based on stochastic dynamic programming is developed to derive efficiency frontiers for the trade-off between the long-run average social benefits and price variation. The method is used to quantify the importance of price variation per se as a criterion in U.S. wheat storage policy. The results suggest that a single criterion of maximum expected social benefits, calculated by the traditional surplus measures, is satisfactory because price variation is incidentally reduced enough that further reductions can be attained only a considerable opportunity cost. |
| Tipo: Journal Article |
Palavras-chave: Crop Production/Industries. |
| Ano: 1982 |
URL: http://purl.umn.edu/32413 |
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| Burt, Oscar R.; Worthington, Virginia E.. |
| The dynamic structure of wheat acreage supply response is considerably more complex than previous studies have recognized. The distributed lag response is saw-toothed in its pattern, which is believed to eminate from the influence of summer fallow in crop rotations in the Great Plains. The acreage response elasticity estimate for the Great Plains at mean price was 1.3, and for the aggregate U.S. it was 1.5. For the Unites States, the proportion of long-run response experienced over the first five years from an increment to price was .24, .44, .70, .95, and. 99. |
| Tipo: Journal Article |
Palavras-chave: Crop Production/Industries. |
| Ano: 1988 |
URL: http://purl.umn.edu/32160 |
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| Burt, Oscar R.; Taylor, C. Robert. |
| Statistical procedures are developed for reducing the number of autonomous state variables in stochastic dynamic optimization models when these variables follow a stationary process over time. These methods essentially delete part of the information upon which decisions are based while maintaining a logically consistent model. The relatively simple linear autoregressive process as well as the general case is analyzed and the necessary formulae for practical application are derived. Several applications in agricultural economics are discussed and results presented which quantify the relative amount of information sacrificed with the reduction in number of state variables. |
| Tipo: Journal Article |
Palavras-chave: Research Methods/ Statistical Methods. |
| Ano: 1989 |
URL: http://purl.umn.edu/32349 |
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| Burt, Oscar R.; Frank, Michael D.; Beattie, Bruce R.. |
| A heuristic criterion for choosing an acceptable level of bias in ridge regression is presented. The criterion is based on a noncentral F-test of the stochastic restrictions implicit in the ridge estimator. An appropriate significance level for the test is based on conjunctive use of strong and weak mean square error criteria. The procedure is illustrated in estimating a Cobb-Douglas production function for the Central Valley of California using factor shares as priors rather than the null vector. Preliminary results suggest that a conjunctive SMSE/WMSE criterion with more reasonable priors selects an estimator with smaller bias than ridge trace. |
| Tipo: Journal Article |
Palavras-chave: Research Methods/ Statistical Methods. |
| Ano: 1987 |
URL: http://purl.umn.edu/32237 |
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