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Unformatted text preview: ECO321 by H.Morita Economic Statistics II- Practice Final - Part I (1) In a regression: ln Y i = + 1 ln X i + i : a. 1 is an effect of X on Y . b. 1 is a partial effect of X on Y . c. 1 is an elasticity of Y with respect to X . d. 1 has no special meaning. (2) In a regression: Earnings i = + 1 Age i + 2 Female i + 3 ( Age i Female i ) + u i where Female is a binary variable. If you want to test the hypothesis that both genders incomes grow at the same rate but there is a income gaps between genders, then the null hypothesis is: a. 1 = 0 b. 2 = 0 c. 3 = 0 d. 2 = 0 and 3 = 0 (3) The OLS estimators are biased and inconsistent if a. important variable to determine Y is omitted from regressors. b. the variables contain measurement errors. c. there is a simultaneous causality. d. all of above. (4) Sample selection bias occurs when a. the choice between two samples is made by the researcher. b. data are collected from a population by simple random sampling. c. samples are chosen to be small rather than large. d. the availability of the data is influenced by a selection process that is related to the value of the dependent variable. (5) The simultaneous causality bias occurs because a. there are some large outliers in the sample data. b. there is a perfect multicollinearity. c. one or more regressors are correlated with error term. d. i.i.d. is impossible. (6) In the presence of simultaneous causality, we need to choose valid instruments which: a. must be exogenous to the error term. b. must be relevant to some of regressors. 1 c. a. and b. d. a. and homoskedastic error term. (7) Stationarity means that the a. error terms are not serially correlated. b. probability distribution of the time series variable does not change over time....
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- Spring '11