Question threea. The time plot below is for the natural logarithm of quarterly investment in West Germanybetween 1960.1 - 1982.4. 6log investment6.86.66.46.265.85.65.45.250Model A:20Model B:40Observation60Avt = a + pvt-1 +Does the series look stationary? Justify your answer.b. A researcher decides to test whether or not the log investment series in part (a) is stationaryusing the Augmented Dickey-Fuller (ADF) test. He considers basing the test on one of thefollowing two models:Pi=1Ave = a + ßt + pvt-1+0₁ Vt-1 + etР80i=1E0₁ Vt-i + et100[CI-EwhereVtthe basis for the ADF test: Model A or Model B? Be sure to justify your answer.@y₁-₁+1=0denotes the natural logarithm of investment. Which model should the researcher use as

The series does not look stationary. It is because from the given time-series plot it is evident…

Answered: Model A: Observation Does the series…

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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You are using exponential smoothing on an annual time series concerning total revenues (in millions of dollars). You decide to use a smoothing coefficient of W=0.3, and the exponentially smoothed value for 2010 is E2010 (0.3) (12.4) + (0.7)(9.6). a. What is the smoothed value of this series in 2010? b. What is the smoothed value of this series in 2011 if the value of the series in that year is $11.2 million? a. E2010$ million (Type an integer or a decimal. Do not round.) b. E2011 = $ million (Type an integer or a decimal. Do not round.)
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Exercise 3 Ana owns a car worth €50 000 which can get stolen with probability 1%. She can pur- chase coverage of the amount q € [0; 50 000] at premium 7 = €0.05 for each euro covered. Her utility function is u = ln(w), where w is money. Assume she has no other assets. (a) Formulate her maximization problem. (b) How much insurance (q) will she choose to buy? Explain. (c) How much profits does the insurance company earn on insuring Ana?
Consider the following time series data. Week 1 2 3 4 5 6 Value 17 11 15 10 16 13
Actual sales of a company (in millions of dollars) for January through April are shown below. Month Sales January 17 February 26 March 31 April 39 May (a) Use a = 0.3 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Forecast Squared Forecast Error January 17 February 26 March 31 April 39 May MSE = (b) Use a = 0.1 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Forecast Squared Forecast Error January 17 February 26 March 31 April 39 Мay MSE =
How do you calculate an exponential smoothing model using alpha = 0.5 and 0.8. What is the prediction for 7/1/2021 for each technique? Forecasts Date Day of Week Rooms Sold MA 2 MA 4 ES (0.8) ES (0.5) 6/1/2021 Tuesday 123 6/2/2021 Wednesday 109 6/3/2021 Thursday 140 116.0 6/4/2021 Friday 199 124.5 6/5/2021 Saturday 179 169.5 142.8 6/6/2021 Sunday 140 189.0 156.8 6/7/2021 Monday 117 159.5 164.5 6/8/2021 Tuesday 132 128.5 158.8 6/9/2021 Wednesday 108 124.5 142.0 6/10/2021 Thursday 151 120.0 124.3 6/11/2021 Friday 182 129.5 127.0 6/12/2021 Saturday 168 166.5 143.3 6/13/2021 Sunday 161 175.0 152.3 6/14/2021 Monday 119 164.5 165.5 6/15/2021 Tuesday 133 140.0 157.5 6/16/2021 Wednesday 139 126.0 145.3 6/17/2021 Thursday 146 136.0 138.0 6/18/2021 Friday 168 142.5 134.3 6/19/2021 Saturday 188 157.0 146.5…
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The following time-series diagram tracks the performance of two stocks during the month of October. Describe the overall trend of the two stocks. By what percentage did the value of each stock increase or decrease during the month of October? Stock 2 $200 Stock 1 T$150 Stock 2 -$100 Stock 1 T$ 50 1 5 11 16 21 26 31 October a. Stock 2 increased by 200%. Stock 1 decreased by 50%. b. Stock 2 increased by 100%. Stock 1 decreased by 50% c. Stock 2 increased by 200%. Stock 1 decreased by 33% d. Stock 2 increased by 100%. Stock 1 decreased by 33%.
Six years of quarterly data of a seasonally adjusted series are used to estimate a linear trend model as T t=181.60 + 1.44t. In addition, quarterly seasonal indices are calculated as § 1= 0.85, § 2= 0.91, Ŝ 3 = 116, and § 4= 119. a-1. Interpret the first quarterly index. In other words, what is the value of the series in the first quarter as compared to the average? 15% above O 85% below O 15% below 85% above a-2. Interpret the fourth quarterly index. In other words, what is the value of the series in the fourth quarter as compared to the average? O 81% above O 19% above 19% below O 81% below b. Make a forecast for all four quarters of next year. (Do not round intermediate calculations. Round your answers to 2 decimal places.) Quarter 1 Quarter 2 Quarter 3 Quarter 4
Customer interarrival times at a store are exponentially distributed with rate 8 customers per hour. Customers are served by a single customer service representative (CSR), with an average service time of 5 minutes. Service times are also exponentially distributed. Which of the following statements are true? (Please tell me why option B is the right option) A. The average time spent waiting for the CSR is 15 minutes. B. The store is busy (has customers) 33% of the time C. The probability of having one or more customer in the store is 0.222. D. None of the answers are correct. E. The average number of customers in the store is 2.0.
The world population over a period of 250 years has been estimated by the U.N. (see table below). Year Population [billion] 1800 1.00 1850 1.30 1900 1.70 1950 2.50 1970 3.70 1990 5.30 2005 6.50 2020 7.60 2050 9.10* *) prediction Source: U.S Census Bureau and UN. A suggested model to describe the population over time is: P= L 1+ e-k(x-xo) Where p is the population, x is the year and L, k and x, are constants. I represents the all time maximum of the population. a) Fit the model to the data using non-linear regression using initial guesses L = 20, k = 0.01 and x) = 2050. What will be the all time maximum of the world population according to this model? b) Find the coefficient of determination (R²) for the fit. (If you did not answer question a), you may use the initial guesses as parameter values).

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