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Find the OLS estimators for the parameters using the matrices above.
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- 4- The manager of Collins Import Autos believes the number of cars sold in a day(Q) depends on two factors: (1) the number of hours the dealership is open (H) and (2) the number of salespersons working that day (S ). After collecting data for two months (53 days), the manager estimates the following log-linear model: Q = aHbSc ----- a. Explain how to transform this log-linear model into a linear form that can be estimated using multiple regression analysis. b. How do you interpret coefficients b and c? If the dealership increases the number of salespersons by 20 percent, what will be the percentage increase in daily sales? c. Test the overall model for statistical significance at the 5 percent significance level.tate whether the following statements are true or false with a brief explanation: a) Logit model is estimated by minimising the sum of the squares residuals of the model. b) In difference-in-differences analysis, the assumption of ‘parallel trends’ is generally testable. c) Suppose you have estimated a model Y = 0.2 – 0.7D + 2X + 0.4X*D. Y and X are continuous variables and D is a dummy variable. If D=1, the marginal effect of X on Y is always larger, and therefore the predicted Y is always larger, than in the case where D=0. d) The first order autoregressive model can be stationary or non-stationary. e) The bias in Instrumental Variables estimator depends on the number of observations.Consider the following Stata output for the model of house prices: lhprice=β0+β1bdrms+β2llotsize+β3lsqrft+u estimated on a random sample of 86 houses. You may assume the Gauss Markov Assumptions hold. The variables are defined as: lhprice = the natural log of the house price bedrms = the number of bedrooms llotsize = the natural log of the land or lot size lsqrft = the natural log of the floor space of the house in square feet. Source SS df MS Model Residual 4.65621742 2.09289629 3 82 1.55207247 0.025523126 Total 6.74911372 85 0.079401338 lprice coef. std. err. t P > |t| [95% Conf. Interval] bdrms 0.0581214 -0.0055282 0.121771 llotsize 0.1494716 0.0616548 0.2372884 lsqrft 0.636171 0.421989 0.850353 _cons -0.7083136 -2.221521 0.8048935 Number of obs = 86 F (3, 82) = 60.81 Prob > F = 0.0000 R-squared = 0.6899 Adj R-squared = 0.6786 Root MSE = 0.15976 Does…
- Interpret this graph according to the theory of hecksher-ohlin model.Consider the following Model (notation is standard):C=c(y-τ) I=i(r) y = C+I+G Md/P = L(y, r) Md= Ms=My = f(n) n = h(W/P) f´(n) = W/P Calculate the effects of a change in τ on C, I, r, y and P.The 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?
- QUESTION 6 Which one of the following statements about the modified Stackleberg model is correct? A. None of the other statements is correct. B. Entry accomodation is always more profitable than entry deterrence. C. Whether entry deterrence is more profitable than entry accomodation can depend on the size of the fixed cost. D. Entry deterrence is always more profitable than entry accomodation.Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?Suppose you decide to estimate a student consumption function. After you run an OLS regression on your data set with 36 observations, you obtain the following. The estimated regression, along with standard errors and t-statistics, CO = - 47.143 + 0.9714 YD (se) (2.0307) (0.157) (t) ( ) (6.187) Where, CO : the average annual consumption expenditures of the students on items other than tuition and room. YD : the average annual disposable income (including gifts) of the students a) Interpret the slope and the intercept. b) Compute the test statistics ( t value and critical t ) for the intercept of the regression. Note that significance level is 0.10. c) Suppose that disposable income is increased by 1000 dollars on average. What would be the predicted consumption expenditures?
- Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.In general, what is true about the relationship between the Sum of Squared Residuals in the restricted and unrestricted model? a. SSRr = R-squared * SSRur b. SSRr < SSRur c. SSRr > SSRur d. SSRr = SSRurIn attempting to formulate a model of the passenger arrival data on cruise ships over time would a nonlinear (perhaps a multiplicative exponential) model be preferable to a linear model of cruise ship arrivals against time? What about in the case of the passenger arrivals by ferry against time?