G FTSE 1006. Consider the OLS estimator of a model to explain the stock prices ofobservations from 2012 m1 to 2021 m 12, all variables are calculatedcompany using 120at the end of the month tlog (stock +) 0.86 +0.86 + 0.54 log (profit 6) - 0.65 log (research t)-1.34 log (marketing 6) (6.1)(1.12) (0.24)(0.30)(0.12)3n = 120,R² = 0.34SSR = 1.29F = 3.99:/" 116where stuck tthe›in millionsofstock price in GBP (British Sterling) profit & (the profit before taxGBP), research & (expenditure on research and development), and marketing tmarketing) are measured inparentheses, SSR is the Sum of Squared Residuals, and the I statistic for the significanceGBP. Standard errors are reported in(expenditureonmillionsofoftheregression is$provided.lat What is the interpretation of the coefficient onas you would expect? Explain your answer.log (profit t) 7 Is the sign of coefficientmeasured in thousands of GBP? Explain your answer. Hint:Wouldyour interpretation change is(b) Looking at the estimates, a colleague claims that the effect of marketing expenses is more thanGBP? Explain your answer. Hint: a million is thousand *thousandtheprofit istwice as large as the effect of research and development expensesa suitable test to examine this claim. Clearly specify the null and the alternativeresearch and development expenses on the stock price. Describeand assumptions underlying your test. Indicate additional information, if necessary, to conduct suchhypothesestest.(c) Anothe colleague getsholdoftqa variable smallsmall, random firm in Bulgariawhich captures the monthly stock price ofBulgaria from 2012 m 1 to 2021m 12. Excited with the discovery,the colleague insists unincluding additional variables in the model. What are the likely effects of the inclusion of small tincluding smallt in the model. Describe the statistical reasoning behindon the properties of the OLS estimators of the parameters of the model? Explain your answersintuitively. Hint: the Bulgarian company is completely unrelated with the FTSE companymodel.in the

Given regression model: Here, stock t is the stock price Profit t is the profit before tax in…

Answered: 5. Consider the OLS estimator of a…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Suppose a data set of 200 observations (n = 200) was analyzed using OLS to examine the relationship between CEO salary and various measures of firm performance. The regression results are as follows, with standard errors in parentheses: logy = 5 + 0.2logx₁0.03x₂ +0.002x3 (0.2) (0.04) (0.004) where? (0.009) y = CEO salary in thousands of dollars X₁ = annual firm sales X₂ = percentage of sales lost to competitors X3 = return on stock in percent R² = 0.290 Suppose you want to test the null hypothesis that percentage of sales lost to competitors has no ceteris paribus effect on the salaries of CEOS. For the one-sided alternative hypothesis ß₂ < 0 and 1% rejection rule (i.e., at the 1% level), you would_ the null hypothesis that ß₂ = 0.
Suppose sales for the past 6 months have been94, 91, 102, 99, 109, and 118 Using a smoothing coefficient of 0.7, what is the exponentially smoothed value for the 4th period? (please round your answer to 1 decimal place)
12) The following results are an autoregression for US Exports to Mexico where the dependent variable is the lagged value of US Exports. a) Fill in the table b)Based on these regression results, what is your forecast of US Exports to Mexico for March 2005? c) Which of the two forecasts do you think is more accurate? Explain.
A survey was conducted and asked: "Do you consider your health to be poor?" This was coded as 1 for "yes" and 0 for "no." The effect of age (in years) and sex (1 male, 0 = female) on the outcome was examined using logistic regression. The fitted logistic model is: = logit(ô) = −0.544 + ß * sex + 0.0331 * age (a) What are the odds of responding "yes" for a female subject who is 60 years old? (b) What is the estimated odds ratio corresponding to the age variable? Interpret this estimate. (c) Suppose that, based on the data from the survey, the odds of responding "yes" for males are 3.79% greater than the odds for females, adjusting for age. What is the value of Â? (d) If we only wanted to assess the association between survey response (yes/no) and sex, what other method(s) (that we have learned in this class) could we have used (name at least one)? In your own words, explain why we cannot use the same method(s) if we want to consider the effects of both sex and age on survey response.
Exercise 9. Suppose that the lifespan (in years) of a water heater is modeled as Y - Expon(mean = 8). (a) Given that the unit is working at 3 years, compute the chances it will fail in the next year. (b) Given that the unit is working at 18 years, compute the chances it will fail in the next year. (c) Comment on the reasonableness of this Exponential model.
PQ3
Suppose sales for the past 6 months have been100, 90, 84, 113, 92, and 96 Using a smoothing coefficient of 0.5, what is the exponentially smoothed value for the 4th period? (please round your answer to 1 decimal place)
please answer these two correctly;
Here's a table with average monthly price of a stock from Jan 2021-July 2021. Month Price Jan 29000 Feb 27000 Mar 31000 Apr 30000 May 32000 June 28000 July 29000 Using a 3-period weighted moving average with weights (wt, wt-1, W-2) = (0.6, 0.2, 0.2), the predicted price August is Using an exponential smoothing model with = 0.4, and with the initial predicted price for Jan the same as the observed price for January, the predicted price for August is % Now, let's suppose that we finally observe that the price for August is 30000. The absolute percent error (APE) of your prediction for August is using the weighted moving average method and % using the exponential smoothing model. (Note: Round your answer to the nearest 2 decimal places. For your APE answer for example let's say you get 0.06786 from Excel, then you should type-in 6.79 as your answer here since it is represented as percentage. Do not enter any symbols other than integers and decimal.)
14. This table shows the growth in the number of free wireless Internet access points in a city. Year 2006 2007 Number of access points 2003 2004 29 46 119 190 Determine the exponential relation y = ab* that best fits the data, where x is the number of years since 2003. a. y = 29(1.4)* b. y 18(1.8)* C. y = 18(1.8)* d. y = 29(1.6) 15. This table shows the decay in mass of a radioactive 5 15 Time (days) Mass (mg) 159 81 10 114 2005 74 Estimate the mass of the element after 45 days. a. About 15 mg b. About 11 mg 20 58 C. element. 25 41 About 21 mg E OF d. About 8 mg
One of the possible models for the coronavirus is the logistic curve, shown here without scale. This curve shows the total number of cases of people infected with the virus. The rate of change for this curve is the number of ca es per day. 1. On the curve, mark the point at which the number of cases per day is greatest. What is a mathematical term for this point? 2. One of the criteria for re-opening activities is fourteen consecutive da s with a decreasing number of cases. Where is this on the curve? Number of Infected people as a function of time

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