ppose you have two independent unbiased estimators of the same parameter, θ, say θandθ, with different variances, v ∧ 1 ∧ 2 1 and v2. What linear combination, = cθ∧ 1θ∧ 1+ c2θ∧ 2 is the minimum variance unbiased estimator of θ?

What are the three Gauss–Markov conditions? Explain.

A consumer buys (x1, X2 , X3 ) at price vector (P,, P2 , P3). We make three observations of his behavior: At (P,P,,P;) = (1,1,2), the consumer chooses (x,,x,,x3) = (5,19,9) At (P, P,,P,) = (1,1,1), the consumer chooses (x,,x,, x3 ) = (12,12,12) At (P, P,,P,) = (1, 2,1) , the consumer chooses (x,,x,,x; ) = (27,11,1) %3D %3D Does the consumer's behavior satisfy WARP? What about SARP?

Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables ● marginį - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to O when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin, = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: . regress margin home if team ==== "Brooklyn Nets". Source Model Residual Total margin home __cons SS 1459.95122 15252.0488 16712 df 1 80 Coef. Std. Err. MS 81 206.320988 8.439024 3.049595 -5.219512 2.156389 1459.95122 190.65061 t Number of obs F(1, 80) Prob > F R-squared P>|t| 2.77 0.007 -2.42 0.018 = Adj R-squared = Root MSE = 2.370137 -9.510864 = 82 7.66 0.0070 0.0874 0.0760 13.808…