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DAIRY FARM SIZE, ENTRY, AND EXIT IN A DECLINING PRODUCTION REGION AgEcon
Rahelizatovo, Noro C.; Gillespie, Jeffrey M..
As with most agricultural industries, the U.S. dairy industry has evolved into a structure including fewer yet larger firms. In Louisiana, total milk production has declined along with dairy farm numbers since 1972. This study addresses the impact of alternative policies, macroeconomic factors, and technology on the structure of the Louisiana dairy industry using a micro-data non-stationary Markov chain analysis. Results indicate that a number of factors have affected the structure of the industry in Louisiana, including but not limited to prices, milk supply reduction programs, technology and interest rates.
Tipo: Journal Article Palavras-chave: Dairy farms; Markov chain analysis; Seemingly unrelated regression; Marketing.
Ano: 1999 URL: http://purl.umn.edu/15372
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ENTROPY-BASED SEEMINGLY UNRELATED REGRESSION AgEcon
Harmon, Alice; Preckel, Paul V.; Eales, James S..
We adapt the classical SUR procedure to a minimum cross entropy approach to estimate linear systems of equations where the errors across equations are correlated. We conclude that our entropy-based approach may provide a reasonable substitute for SUR in cases where classical methods may not be applied due to shortages of data.
Tipo: Working or Discussion Paper Palavras-chave: Entropy; Econometrics; Seemingly unrelated regression; Research Methods/ Statistical Methods.
Ano: 1998 URL: http://purl.umn.edu/28682
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From the help desk: Seemingly unrelated regression with unbalanced equations AgEcon
McDowell, Allen.
This article demonstrates how to estimate the parameters of a system of seemingly unrelated regressions when the equations are unbalanced, i.e., when the equations have an unequal number of observations. With estimators that require the data to be in wide format, such as Stata’s sureg, the equations must be balanced. Any additional observations that are available for some equations, but not for all, are discarded, potentially resulting in a loss of efficiency. Reshaping and scaling the data allows us to use Stata’s xtgee command to fit the model and obtain estimates utilizing all the available data. The resulting estimator is potentially more efficient when the equations are unbalanced.
Tipo: Journal Article Palavras-chave: SUR; Seemingly unrelated regression; Unbalanced equations; Generalized estimating equations; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116272
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