Sketched out answers Final Econ140A 2013Fall

The probability of survival for a female who paid a

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Unformatted text preview: probability of survival for a female who paid a $400 fare is ( ) ( ) d. The probability that a female who paid a $400 fare survived is 142% 2 page 3 e. No, this does not make sense because we cannot have probabilities greater than 1. A probit model would restrict the predicted probability to somethin g between 0 and 1 for any values of the right hand side variables. 6. ̂ therefore, ̂ () which implies: ̂ () or ̂ ̂ b. There are a couple of ways to do this. The easy one is to use the theorem (̂ ) ̂ )= ̂) ( ( c. We know that the formula for the reported variance is and that ̂ () therefore, the denominator is unchanged. What about ? For Data set 1 the residuals are , and for the Data set 2 they are . , so the value of for ̂ a. is equal to 16 . Thus the reported standard error for ̂ is √ . d. The t-stat is just the ratio of the coefficient and the standard error so the t -stat for the same as the t stat for . 7. a. ( ̂ ) ̂ Therefore, ̂ is consistent. b. This is just the standard OLS estimator, but our “Y” variable is now “X” and our RHS variable is “Z”: ̂ c. Similarly, is also a valid equation to estimate (this is often called the reduced form equation). Write down the OLS estimator ̂ . As above, the OLS estimator for is: ̂ ̂ d. Show that the ratio ̂ is equal to ̂ . 3 is page 4 ̂ ̂ 8. a. b. , [ [ [ c. ( ̂ ) ( ) [ ̂] d. ( ̂) [( ̂ [ ̂ ]) ] [( ̂ *Note that all of the terms with so… )] [( )] are 0 after taking the expectation and that ( ( ) ) ( 4 )...
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This test prep was uploaded on 03/19/2014 for the course ECON 140a taught by Professor Staff during the Spring '08 term at UCSB.

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