1. Suppose you observe (Y, X1, X2, ..., xk) for N observations. For each observation 2, the model + Bkki + €į. Write the model out in matrix form. What are the is Yi = Bo + B1x1i + B₂x2 i + dimensions of Y. X. B. €?
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- 8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OOThe Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?Consider the OLS estimator 3;. Under the Gauss-Markov assumptions, O the estimator is the best linear unbiased estimator. O the estimator is asymptotically normally distributed. O the estimator has the properties stated in the other three possible answers. O the estimator is consistent.
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