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Identifying Regression Parameters When Variables are Measured with Error

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Abstract

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.

Suggested Citation

  • Alicia Rambaldi & T. H. Y. Tran & Antonio Peyrache, 2016. "Identifying Regression Parameters When Variables are Measured with Error," Discussion Papers Series 557, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uq2004:557
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    File URL: https://economics.uq.edu.au/files/46125/557.pdf
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    More about this item

    Keywords

    unobserved components; time-varying parameters; least squares bias; returns to scale; technological change;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

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