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2012 (6)

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Hadoop: the definitive guide : third edition
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ISBN: 9781449311520 1449311520 Year: 2012 Publisher: Sebastopol O'Reilly Media

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Storage and analysis at internet scale


Book
Plant-level Productivity and Imputation of Missing Data in U.S. Census Manufacturing Data
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Year: 2012 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Plant-level Productivity and Imputation of Missing Data in U.S. Census Manufacturing Data
Authors: --- ---
Year: 2012 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Within-industry differences in measured plant-level productivity are large. A large literature has been devoted to explaining these differences. In the U.S. Census Bureau's manufacturing data, the Bureau imputes for missing values using methods known to result in underestimation of variability and potential bias in multivariate inferences. We present an alternative strategy for handling the missing data based on multiple imputation via sequences of classification and regression trees. We use our imputations and the Bureau's imputations to estimate within-industry productivity dispersions. The results suggest that there may be more within-industry productivity dispersion than previous research has indicated.


Book
Changing farm structure and the distribution of farm payments and federal crop insurance
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Year: 2012 Publisher: [Washington, D.C.] : U.S. Dept. of Agriculture, Economic Research Service,

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Book
Changing farm structure and the distribution of farm payments and federal crop insurance
Authors: --- ---
Year: 2012 Publisher: [Washington, D.C.] : U.S. Dept. of Agriculture, Economic Research Service,

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Book
Plant-level Productivity and Imputation of Missing Data in U.S. Census Manufacturing Data
Authors: --- --- ---
Year: 2012 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Abstract

Within-industry differences in measured plant-level productivity are large. A large literature has been devoted to explaining the causes and consequences of these differences. In the U.S. Census Bureau's manufacturing data, the Bureau imputes for missing values using methods known to result in underestimation of variability and potential bias in multivariate inferences. We present an alternative strategy for handling the missing data based on multiple imputation via sequences of classification and regression trees. We use our imputations and the Bureau's imputations to estimate within-industry productivity dispersions. The results suggest that there is more within-industry productivity dispersion than previous research has indicated. We also estimate relationships between productivity and market structure and between output prices, capital, and the probability of plant exit (controlling for productivity) based on the improved imputations. For some estimands, we find substantially different results than those based on the Census Bureau's imputations.

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