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Multiple Linear Regression
Topics in Regression Analysis
Model and Identification
8 other sections not shown
2SLS applied approximate assumed assumption asymptotic distribution automobiles autoregressive Bayesian capita Chap Chow column vector components compute condition consistent estimator consumption converges in probability correlation covariance matrix covariance-stationary defined demand function denotes density function dependent variable derivatives desired stock diagonal econometric econometric model Econometrica economic elements endogenous variables equal error evaluate explain explanatory variables FIML estimator given income instrumental variables iteration lagged lagrangian multiplier least-squares estimator likelihood function linear regression linear regression model log-likelihood function logit model maximize maximum maximum-likelihood estimator mean zero method multiplier Newton-Raphson method nonlinear normal distribution null hypothesis observations obtained optimal plim positive-definite matrix predetermined variables prior density Prob probability limit problem reduced form reduced-form equations regression coefficients residuals respect restrictions sample simultaneous equations solution solve statistic stochastic structural equation sum of squares Theorem unbiased unknown zero and covariance