Given the following regression: wagei = β0 + β1edui + β2experi + β3exper2i + β4exper3i +...

Given the following regression:

wagei = β0 + β1edui + β2experi + β3exper2i + β4exper3i + ui

wagei = -4.146156 + .5959117edui + .3191707experi - .0074834exper2i + .0000415exper3i + ui

Which order of polynomial (i.e., J) best describes the relationship between work experience and wages? Please explain your answer.

Using your model above), what is the marginal effect of experience when experience increases from 9 to 10 years? What is the marginal effect of experience when experience increases from 25 to 26 years? Does experience exhibit diminishing marginal returns?

Using your model above, find the average difference in wages between someone with 15 years of experience and someone with 23 years of experience, holding education constant. Is the difference statistically significant?

Expert Solution

orchestra answered 1 year ago

(2) Suppose the original regression is given by y = β0 + β1x1 + β2x2 +...

(2) Suppose the original regression is given by y = β0 + β1x1 + β2x2 + β3x3 + u. You want to test for heteroscedasticity using F test. What auxiliary regression should you run? What is the null hypothesis you need to test?

8) Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + ...

8) Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + β5X5i + ui This model has been estimated by OLS. The Gretl output is below. Model 1: OLS, using observations 1-52 coefficient std. error t-ratio p-value const -0.5186 0.8624 -0.6013 0.5506 X1 0.1497 0.4125 0.3630 0.7182 X2 -0.2710 0.1714 -1.5808 0.1208 X3 0.1809 0.6028 0.3001 0.7654 X4 0.4574 0.2729 1.6757 0.1006 X5 2.4438 0.1781 13.7200 0.0000 Mean dependent var 1.3617 S.D. dependent...

Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i ...

Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + ui This model has been estimated by OLS. The Gretl output is below. Model 1: OLS, using observations 1-59 coefficient std. error t-ratio p-value const -0.1305 0.6856 -0.1903 0.8498 X1 0.1702 0.1192 1.4275 0.1592 X2 -0.2592 0.1860 -1.3934 0.1692 X3 0.8661 0.1865 4.6432 0.0000 X4 -0.8074 0.5488 -1.4712 0.1470 Mean dependent var -0.6338 S.D. dependent var 1.907 Sum squared resid 143.74 S.E. of...

You have the following regression model. y = β0 + β1x1 + β2x2 + β3x3 + u

You have the following regression model. y = β0 + β1x1 + β2x2 + β3x3 + u You are sure the first four Gauss-Markov assumptions hold, but you are concerned that the errors are heteroskedastic. How would you test for hetereskedasticity? Show step by step.

simple linear regression proof of variance of intercept estiamtor β0

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x + β2d1 + β3d2 + ε. Coefficients Standard Error t Stat p-value Intercept −0.82 0.25 −3.28 0.0020 x 3.36 1.20 2.80 0.0074 d1 −15.41 16.75 −0.92 0.3624 d2 8.28 2.40 3.45 0.0012 a. Compute yˆy^ for x = 312, d1 = 1, and d2 = 0; compute yˆy^ for x = 312, d1 = 0, and d2 = 1. (Round your answers to 2...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x + β2d1 + β3d2 + ε. Coefficients Standard Error t Stat p-value Intercept −0.61 0.25 −2.44 0.0186 x 2.86 1.04 2.75 0.0085 d1 −13.09 15.40 −0.85 0.3997 d2 6.15 2.05 3.00 0.0043 a. Compute yˆ for x = 260, d1 = 1, and d2 = 0; compute yˆ for x = 260, d1 = 0, and d2 = 1. (Round your answers to 2...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x + β2d1 + β3d2 + ε. Coefficients Standard Error t Stat p-value Intercept −0.77 0.25 −3.08 0.0035 x 3.30 1.25 2.64 0.0113 d1 −13.30 17.50 −0.76 0.4511 d2 5.45 1.25 4.36 0.0001 a. Compute yˆy^ for x = 232, d1 = 1, and d2 = 0; compute yˆy^ for x = 232, d1 = 0, and d2 = 1. (Round your answers to 2...

Suppose you estimate the following regression model using OLS: Yi = β0 + β1Xi + β2Xi2...

Suppose you estimate the following regression model using OLS: Yi = β0 + β1Xi + β2Xi2 + β3Xi3+ ui. You estimate that the p-value of the F-test that β2= β3 =0 is 0.01. This implies: options: You can reject the null hypothesis that the regression function is linear. You cannot reject the null hypothesis that the regression function is either quadratic or cubic. The alternate hypothesis is that the regression function is either quadratic or cubic. Both (a) and (c).

In a simple linear regression model yi = β0 + β1xi + εi with the usual...

In a simple linear regression model yi = β0 + β1xi + εi with the usual assumptions show algebraically that the least squares estimator β̂0 = b0 of the intercept has mean β0 and variance σ2[(1/n) + x̄2 / Sxx].

Question

Given the following regression: wagei = β0 + β1edui + β2experi + β3exper2i + β4exper3i +...

Solutions

Expert Solution

Related Solutions

(2) Suppose the original regression is given by y = β0 + β1x1 + β2x2 +...

8) Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + ...

Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i ...

You have the following regression model. y = β0 + β1x1 + β2x2 + β3x3 + u

simple linear regression proof of variance of intercept estiamtor β0

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...

Using 50 observations, the following regression output is obtained from estimating y = β0 + β1x...

Suppose you estimate the following regression model using OLS: Yi = β0 + β1Xi + β2Xi2...

In a simple linear regression model yi = β0 + β1xi + εi with the usual...