7. Classical linear model assumptions for time series Consider the following stochastic process {(x1, x2, x3, . . . , xk, yt): t = 1, 2, . . . , n}{(x1, x2, x3, . . . , xk, yt): t = 1, 2, . . . , n} that follows the linear model: y=β0+β1xt1+β2xt2+β3xt3 + . . . +xtk+uy=β0+β1xt1+β2xt2+β3xt3 + . . . +xtk+u {(ut: t =1, 2, . . . , n)}{(ut: t =1, 2, . . . , n)} = sequence of error terms nn = number of observations (time periods) What are the minimum Gauss–Markov assumptions needed for the OLS estimates of βˆjβ^j , for j = 1, 2, . . . , kj = 1, 2, . . . , k , to be the best linear unbiased estimators (BLUE) conditional on the explanatory variables for all time periods ( XX )? Check all that apply.

Options are missing.

The options for the above question are:

TS.1: Linear in parameters.

TS.2:No perfect collinearity

TS.3: Zero conditional mean.

TS.4: Homoskedasticity.

TS.5: No serial correlation

TS.6: Normality.

Hence the correct answer is TS1 to TS 5

Step-by-step explanation:

Assumptions TS 1 to TS 5 are the minimum set of assumptions needed to for the OLS estimates to be the best linear unbiased estimators conditional on explanatory variables for all time periods.

The assumptions of Normality is not needed for the estimators to show the BLUE property

answer: 204

step-by-step explanation:

we are told that the revenue is

x⋅(4080−10x)=−10x^2+4080x

this is a quadratic function, so to maximize it we should find the x-value of its vertex. recall that the x-value of the vertex of the parabola ax^2+bx+c is given by the formula −b/2a.

so in this case, we have a=−10 and b=4080, so the x-value of the vertex is

−b/2a= −(4080)/2(−10)=204