Identifying Regression Parameters When Variables are Measured with Error
Alicia Rambaldi (),
T. H. Y. Tran and
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T. H. Y. Tran: Department of Economics, Yale University
Antonio Peyrache: School of Economics, The University of Queensland, http://www.uq.edu.au/economics/
No 557, Discussion Papers Series from University of Queensland, School of Economics
The paper proposes an approach for identifying and estimating the economic parameters of interest when all the variables are measured with errors and these are correlated. Two propositions show how the parameters of interest and the bias are identified. Three Monte Carlo simulations illustrate the results. The empirical application estimates returns to scale and technological progress in US manufacturing sectors. The results can be linked to previous works in the literature to demonstrate the ambiguous bias in least squares estimates of returns to scale parameters and to compare estimates of trends in technological change using two alternative identification approaches.
Keywords: unobserved components; time-varying parameters; least squares bias; returns to scale; technological change (search for similar items in EconPapers)
JEL-codes: C18 C32 E23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-pr~
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Persistent link: https://EconPapers.repec.org/RePEc:qld:uq2004:557
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