We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings.
We use the following model to estimate the effect of a training program on annual earnings in 2018:
ln(earn2018)=β0+β1train+β2ln(earn2016)+β3educ+β4exper+u
where
- earn2018 = individual total annual earnings in 2018 in dollars
- train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise
- earn2016 = individual total annual earnings in 2016 in dollars
- educ = the individual's years of education
- exper = the individual's years of experience
Now, I want to test whether the effect of an additional year of education increases earnings by twice as much as an additional year of experience. My null hypothesis is H0:β3=2β4.
To get the standard error I need to conduct this hypothesis test, I rearrange or re-parameterise the model so when I estimate this new model I will have a coefficient estimate and its standard error that will allow me to test this hypothesis.
Which of the following is the correct re-parameterised model?
a) ln(earn2018) = β0 + β1train + β2ln(earn2016) + (θ+2β4)educ + (θ-0.5β3)exper + u. where θ = β3 - 2β4
b) none of the answers are correct
c) ln(earn2018) = β0 + β1train + β2ln(earn2016) + θ(educ + 2exper) + β4exper + u. where θ = β3 - 2β4
d) ln(earn2018) = β0 + β1train + β2ln(earn2016) + θeduc + β4(2educ + exper)+ u. where θ = β3 - 2β4
e) ln(earn2018) = β0 + β1train + β2ln(earn2016) + (θ - 2β4)educ + β4exper + u. where θ = β3 - 2β4
Step by stepSolved in 2 steps
- Note: If your answer does not exactly match the correct choice, it is due to rounding of intermediate calculations. To avoid the discrepancy, do your calculations in Excel without rounding. A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of households, the company collected the accompanying data. The data are in units of thousands of dollars. INSUR INCOME 97 38 141 29 y = X = Let INSUR 280 75 %3D INCOME 303 81 453 137 357 77 199 43 251 53 807 184 147 45 272 70 537 128 527 117 245 55 483 116 673 204 194 46 154 51 163 48 2 The denominator of the slope coefficient formula for the estimated regression equation is: 108,450.87 280 69 a 507 140 b 105,805.73 464 136 103,225.10 321 71 d 100,707.41 873 206 476 144 574 111 251 65 497 130 826 171 133 32 259 82 281 73 446 146 332 77 219 48 208 55 180 48 169 42 273 69 502 127 547 126 281 80 428 143 370 77 221 49 214 51arrow_forwardNonearrow_forwardTextbook authors must be careful that the reading level of their book is appropriate for the target audience. Some methods of assessing reading level require estimating the average word length. We've randomly chosen 20 words from a randomly selected page in Intro Stats and counted the number of letters in each word: 5, 5, 2, 11, 3, 5, 6, 8, 5, 4, 7, 2, 9, 4, 8, 10, 4, 7, 6, 9 Suppose that our editor was hoping that the book would have a mean word length of 6.8 letters. Does this sample indicate that the authors failed to meet this goal? With a significance level of 0.05, test an appropriate hypothesis and state your conclusion. (i.e state the appropriate null and alternative hypotheses, calculate the test statistic, conclude and interpret it).arrow_forward
- Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.arrow_forwardThe question is the last bolded test. You need the previous questions to answer the last. Thank you for your time and help. You are the manager of a firm that sells CR2032 batteries for car key fobs and other electronic goods. You typically sell the fobs for $7 for a four-pack and sell an average of 27,192 four-packs per month. Due to increased labor costs, you decide to raise the price to $9 per four-pack. When you do this your monthly sales fall to 22,248 four-packs Assuming that your firm’s demand function is linear (i.e., takes the form ), calculate the linear demand function 22,248-27,192/9-7= -4944/2= -2472 Demand equation as 27,192 = a-2472(7) => a=27192+17304 =>a=44,496 27192= -2472x7+b 27192= -17304+b 27192+17304=44,496 Demand function is Q= -2472P+44,496 . Using the demand function that you estimated in the previous problem Calculate demand and total revenue when the price is $7 Q= -2472x7+44,496= -17,304+44,496= 27,192 Total revenue=PxQ=7x27,192=…arrow_forwardWe have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings. We use the following model to estimate the effect of a training program on annual earnings in 2018: ln(earn2018)=β0+β1train+β2ln(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education exper = the individual's years of experience We find: ln(earn2018)^= 11.672 + 0.041train + 0.821ln(earn2016) + 0.037educ + 0.012exper (5.864) (0.019) (0.258) (0.013) (0.007) n=1278, R2= 0.048 Which of the following is the correct interpretation of the…arrow_forward
- The management of a large firm would like to know something about employee morale and the other variables to which it may be related. To study these relationships, each employee in a simple random sample of 27 employees is interviewed and tested to obtain information concerning morale level and the average number of satisfying extra work social contacts experienced during a week. Whether or not the duties the employee usually performs are appropriate to the job title is also determined. The dependent variable Y is measure of morale, the quantitative independent variable, X1 is average number of social contacts, and the following dummy variable coding is used to quantify the qualitative variable: X₂ = 1 if yes, X₂ = 0 if no. The model including dummy variable and the interaction term was being estimated and a part of computer outputs is below:arrow_forwardQuestion 7. Given that and êt is found to be stationary. Yt = Bxt + Et a) What are the implications of this finding? t=1,2,..., T b) Write down the steps and the possible test involved in determining stationarity in êt.arrow_forward
- Principles of Economics (12th Edition)EconomicsISBN:9780134078779Author:Karl E. Case, Ray C. Fair, Sharon E. OsterPublisher:PEARSONEngineering Economy (17th Edition)EconomicsISBN:9780134870069Author:William G. Sullivan, Elin M. Wicks, C. Patrick KoellingPublisher:PEARSON
- Principles of Economics (MindTap Course List)EconomicsISBN:9781305585126Author:N. Gregory MankiwPublisher:Cengage LearningManagerial Economics: A Problem Solving ApproachEconomicsISBN:9781337106665Author:Luke M. Froeb, Brian T. McCann, Michael R. Ward, Mike ShorPublisher:Cengage LearningManagerial Economics & Business Strategy (Mcgraw-...EconomicsISBN:9781259290619Author:Michael Baye, Jeff PrincePublisher:McGraw-Hill Education