Unformatted text preview: Thus, extremely small and large errors occur much more frequently with this density than would happen if the errors were normally distributed. ±ind the score function g n ( θ ) where θ = ± β α ² . 3. Consider the model classical linear regression model y t = x t β + e t where e t ∼ IIN ( 0, σ 2 ) . ±ind the score function g n ( θ ) where θ = ± β σ ² . 4. Compare the Frst order conditional that deFne the ML estimators of problems 2 and 3 and interpret the differences. Why are the Frst order conditions that deFne an efFcient estimator different in the two cases? 1...
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- Spring '09
- Normal Distribution, ﬁrst order conditions, possibly biased coin, Monte Carlo study