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S Chapter 2 Matrix Algebra
Probability and Distribution Theory
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2SLS analysis assume assumption asymptotic covariance matrix asymptotic distribution autocorrelation autoregressive Chapter characteristic roots chi-squared chi-squared distribution classical regression model coefficients column computed condition consider consistent estimator constant term consumption function converges correlation covariance matrix critical value data set degrees of freedom dependent variable derivatives diagonal distributed lag distributed lag model dummy variable earlier Econometrica Economic efficient estimator endogenous variables estimator of a2 example FGLS estimator forecast given heteroscedasticity homoscedasticity implies income instrumental variables iteration Journal of Econometrics Lagrange multiplier Lagrange multiplier test least squares estimator least squares regression least squares residuals likelihood function log likelihood maximum likelihood estimator mean method nonlinear regression normally distributed observations obtain OLS estimates ordinary least squares plim polynomial positive definite problem produces reduced form regression model regressors restrictions Section seemingly unrelated regressions slope solution standard errors stochastic structure sum of squares Table test statistic transformed unbiased estimator zero