Estimating Economic Relationships under Measurement Error: An Application to the Productivity of US Manufacturing

Tranh, T. H. Y., Rambaldi, A. N. and Peyrache, A. (2015). Estimating Economic Relationships under Measurement Error: An Application to the Productivity of US Manufacturing. In: The 2nd Conference of the International Association for Applied Econometrics (IAAE 2015). Conference of the International Association for Applied Econometrics (IAAE 2015), Thessaloniki, Greece, (). 25-27 June 2015.

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Author Tranh, T. H. Y.
Rambaldi, A. N.
Peyrache, A.
Title of paper Estimating Economic Relationships under Measurement Error: An Application to the Productivity of US Manufacturing
Conference name Conference of the International Association for Applied Econometrics (IAAE 2015)
Conference location Thessaloniki, Greece
Conference dates 25-27 June 2015
Proceedings title The 2nd Conference of the International Association for Applied Econometrics (IAAE 2015)
Publication Year 2015
Sub-type Fully published paper
Open Access Status Not Open Access
Total pages 33
Collection year 2016
Language eng
Abstract/Summary We propose an approach to the problem of measurement errors that evokes long established but rarely used results about the identifiability of the error-in variables (EIV) models. Our approach uses the dynamic structure of the true series and measurement errors to identify the parameters of interest. The dynamics of the underlying time series are introduced into the model using a structural time series approach and the identification of the parameters of interest is achieved by a simple property of the multivariate normal distribution. This modeling framework has several advantages. The first is the possibility of incorporating more flexible components of the time series being studied, such as trends, cycles, and seasonality. The second is that the model allows for a non-zero correlation between the measurement errors of the variables involved in the structural relationship. The third is that using a multivariate normal distribution to derive the structural relationship between variables allows for the time-variation in the relationship, i.e., in both slope and intercept parameters. We prove two results to show our estimator can identify the structural parameters, provide a simulation exercise and an empirical illustration using data from the Bureau of Labor Statistics to compare our findings to those recently presented in Diewert and Fox (2008), where they raise the issue of severe measurement error and the endogeneity of inputs and outputs.
Keyword Unobserved components
Time-varying parameters
Least squares bias
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collections: Non HERDC
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Created: Tue, 27 Oct 2015, 13:22:37 EST by Alys Hohnen on behalf of School of Economics