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- W1603174416 abstract "The discussion in econometric textbooks of estimation problems for regression equations with random measurement errors in the regressor(s) usually leads to two conclusions: (i) the ordinary least squares (OLS) estimator of the regression coefficient(s) is inconsistent, and (ii) the coefficient(s) cannot be identified without having extraneous information in some form. Such extraneous information may, for instance, be the existence of valid instruments for the error-ridden regressor(s). (See e.g., Maddala [1992], Ch. 11.) This discussion, however, relates to situations where the data have either the cross section or the time series format. If a panel data set is available, it may be possible to identify the regression coefficient(s) and estimate them consistently without making use of extraneous information. In this chapter, we discuss the estimation of simple linear economic relationships from panel data with random measurement errors. This is an important problem since panel data in economics will frequently be genuine micro data, i.e., collected from individuals, households, firms, or companies, and in such data sets, errors of measurement may be present and often they may, as a simplification, be treated as random (see e.g.,Griliches [1986]). For discussion of specific examples, see Griliches [1977], [1979] and Griliches and Hausman [1986]. Nonlinear latent variable models for panel data are discussed in Chapter 17." @default.
- W1603174416 created "2016-06-24" @default.
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- W1603174416 date "1996-01-01" @default.
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- W1603174416 title "Panel Data with Measurement Errors" @default.
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- W1603174416 doi "https://doi.org/10.1007/978-94-009-0137-7_10" @default.
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