Least Square Linear Prediction with Two-Sample Data
AbstractThis paper investigates the identification and estimation of the least square linear predictor for the conditional expectation of an outcome variable Y given covariates (X;Z0) from data consisting of two independent random samples; the first sample contains replications of the variables (Y;Z0) but not X, while the second sample contains replications of (X;Z0) but not Y . The contribution is to characterize the identified set of the least square linear predictor when no assumption on the joint distribution of (Y;X;Z0), except for the existence of second order moments, is imposed. We show that the identified set is not a singleton, so the least square linear predictor of interest is set identified. The characterization is used to construct a sample analog estimator of the identified set. The asymptotic properties of the estimator are established and its implementation is illustrated via Monte Carlo exercises.
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Bibliographic InfoPaper provided by Department of Economics, University of Bristol, UK in its series Bristol Economics Discussion Papers with number 12/631.
Length: 30 pages
Date of creation: Nov 2012
Date of revision:
Network Identification; Least Square Linear Prediction; Two samples;
Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-02-08 (All new papers)
- NEP-ECM-2013-02-08 (Econometrics)
- NEP-FOR-2013-02-08 (Forecasting)
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