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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 ( ) where = . 3. Consider the model classical linear regression model y t = x t + e t where e t IIN ( 0, 2 ) . ind the score function g n ( ) where = . 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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This note was uploaded on 09/12/2010 for the course GERAS 099876f taught by Professor Gtewewa during the Spring '09 term at Aberystwyth University.
 Spring '09
 gtewewa

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