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BASIC CONCEPTS OF STATISTICAL INFERENCE
CLASSICAL LINEAR REGRESSION
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2SLS asymptotic covariance matrix asymptotic variance characteristic roots classical least-squares estimators classical linear regression column compute conditional distribution conditional expectation consider consistent estimator contemporaneous defined density function dependent variables derived diagonal elements disturbance econometric endogenous variables event example hypothesis idempotent income joint distribution LI/LGRV linear function linear regression model mass or density method multivariate n x n nonnegative definite nonsingular normal equations normally distributed obtained orthogonal orthogonal matrix partitioned plim positive definite predetermined variables priori probability distribution probability limit properties quadratic form random sampling random variables random vector reduced form reduced-form coefficients regressand regressors relationship residuals restrictions sample mean sample observations sample statistics sample variance sampling distribution scalar Section simply specification standard normal statistical inference stochastic process structural equation structural estimates sum of squares Suppose symmetric matrix tion unbiased estimator uncorrelated values x'Ax zero