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Experimental Derivation of Sampling Distributions
Probability and Probability Distributions
Theoretical Derivation of Sampling Distributions
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acceptance region alternative assume assumptions asymptotic variance asymptotically efficient autoregressive calculated classical normal linear condition confidence intervals consider consistent estimator consumption function correlation corresponding covariance derived desirable properties determined disturbance Econometric elements endogenous variables equal to zero Error Type esti estimator of a2 example explanatory variables Figure finite follows formula given heteroskedastic homoskedasticity income independent instrumental variables least squares estimators least squares method level of significance likelihood function linear regression linear regression model maximum likelihood estimators multicollinearity nonlinear nonstochastic normal linear regression normally distributed Note null hypothesis obtain ordinary least squares parameters plim population mean random variable reduced form regression coefficients regression equation represents respect restricted sample mean sampling distribution Section seemingly unrelated regressions specification standard deviation standard errors stochastic structural equation Suppose Table test statistic Theorem tion unbiased estimator unbiasedness value of R2 variance a2 variance-covariance matrix