A bank predicts the total amount of deposits based on the Federal Reserve Prime Interest Rate. The RSquare for the sample data is O.2759. Interpret the value of R^2 in the context of these data. O 28% of the variability in the Federal Reserve Prime Interest Rate can be explained by the total amount of deposits. 28% of the variability in the total amount of deposits can be explained by the Federal Reserve Prime Interest Rate. O There is a weak, negative relationship between the total amount of deposits and the Federal Reserve Prime Interest Rate. O There is a weak, positive relationship between the total amount of deposits and the Federal Reserve Prime Interest Rate.

A bank predicts the total amount of deposits based on the Federal Reserve Prime Interest Rate. The RSquare for the sample data is O.2759. Interpret the value of R^2 in the context of these data. O 28% of the variability in the Federal Reserve Prime Interest Rate can be explained by the total amount of deposits. 28% of the variability in the total amount of deposits can be explained by the Federal Reserve Prime Interest Rate. O There is a weak, negative relationship between the total amount of deposits and the Federal Reserve Prime Interest Rate. O There is a weak, positive relationship between the total amount of deposits and the Federal Reserve Prime Interest Rate.

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter5: Business And Economic Forecasting

Section: Chapter Questions

Problem 5E: A firm experienced the demand shown in the following table. *Unkown future value to be forecast Fill...

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AD has estimated the following demand relationship for its product over the last four years, using monthly observations: ln Qt = 4.932- 1.238 ln Pt + 1.524 ln Yt-1 + 0.4865lnQt-1(2.54) (1.38) (3.65) (2.87)R2= 0.8738where Q = sales in units, P = price in Rs., Y is income in Rs,000, and the numbers in brackets are t-statistics.a. Interpret the above model.b. Make a sales forecast if price is Rs. 9, income last month was Rs. 25,000 and sales last month were 2,981 units.c. Make a sales forecast for the following month if there is no change in price or income.d. If price is increased by 5 per cent in general terms, estimate the effect on sales, stating any assumptions.
Give typing answer with explanation and conclusion Suppose that the government must undertake an irreversible policy decision regarding the extent of air pollution regulation. The government is making this decision in a situation of uncertainty, however. In particular, there is some probability p that the benefits will remain the same as they are this year for all future years, but there is some probability 1 - p that benefits will be less in all future years. If we take into consideration the multiperiod aspects, should we err on the side of overregulation or underregulation, compared to what we would do in a single-period choice?
kad has estimated the following demand relationship for its product over the last four years, using monthly observations: ln qt = 4.932- 1.238 ln pt + 1.524 ln yt-1 + 0.4865ln qt-1 (2.54) (1.38) (3.65) (2.87) r 2= 0.8738 where q = sales in units, p = price in rs., y is income in rs,000, and the numbers in brackets are t-statistics. a. interpret the above model. b. make a sales forecast if price is rs. 9, income last month was rs. 25,000 and sales last month were 2,981 units. c. make a sales forecast for the following month if there is no change in price or income. d. if price is increased by 5 per cent in general terms, estimate the effect on sales, stating any assumptions.
Suppose that you work for a U.S. senator who is contemplating writing a bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through year 7. The data are: (c1p2) Year Retail Store Sales 1 $1,225 2 1,285 3 1,359 4 1,392 5 1,443 6 1,474 7 1,467 54 Chapter One a. Use the first naive forecasting model presented in this chapter to prepare a forecast of retail store sales for each year from 2 through 8. b. Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8.) c. Calculate the root-mean-squared error for your forecast series using the values for year 2 through year 7. 3. Use the second naive forecasting model presented in this chapter to answer parts (a) through (c) of Exercise 2. Use P 0.2 in…
21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.
The Financial Markets proficiency has been more inclined to interest rates productssuch as loans and term deposits in Zambia than structured products. With the adverseeconomic repercussions that countries, including Zambia experienced during theoutbreak of COVID-19, the market has moved to structured products provision such asoptions, forwards, to mention but a few. The implementation of risk mitigation strategicby most companies made the financial market to enhance structured products that aresuitable to hedge all risks arising from investments and business operations.Assuming Zambeef employees you as the Investment Hedging Director to manage theinvestment in Zambia, you approach your bank, Standard Chartered Bank to structurean option to help hedge Zambeef future shares transactions. You enter into a 5 yearscall option contract at an exercise price of K350 and paid option premium amounting toK50. Three years later, the company has an increase in operational needs and decidesto assess…
It is known that the ABC oil company supplies consumers with 150 tons of products per day. The company produces only two types of products - gasoline and diesel fuel. The limiting transformation ratio of diesel fuel relative to gasoline is 0.5. a) Draw a line of the company's monthly production capacity for gasoline and diesel. b) With the advent of new technologies, production increased by 20%, and the availability of gasoline increased by 30%. Explain how the company's new product line differs from the previous one. c) If we put the gasoline production on the X-axis of the graph, what percentage of the slope capacity of the production line is different from the slope ratio of the previous production line?
13)Assume that the dummy variable Married=1 if married (0if not married),Sex=1 if male (0if male), and that you estimate the equation: log(wage)= B0 + B1 Married + B2*Sex +B3(Married*Sex). The estimated llog wage for an unmarried woman can be estimated by:a.B0 b.B0+B1+B2 c.B0+B1+B2+B3 d.B0+B3
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A firm experienced the demand shown in the following table. *Unkown future value to be forecast Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (with a w=0.9 and a w=0.3). Note The exponential smoothing forecasts may be begun by assuming Y t+1=Yt. Using the forecasts from 2005 through 2009, compare the accuracy of each of the forecasting methods based on the RMSE criterion. Which forecast would you have used for 2010? Why?
Metropolitan Hospital has estimated its average monthly bed needs as N=1,000+9X where X=timeperiod(months);January2002=0 N=monthlybedneeds Assume that no new hospital additions are expected in the area in the foreseeable future. The following monthly seasonal adjustment factors have been estimated, using data from the past five years: Forecast Metropolitans bed demand for January, April, July, November, and December 2007. If the following actual and forecast values for June bed demands have been recorded, what seasonal adjustment factor would you recommend be used in making future June forecasts?
Covid-19 backed by Lockdown generated many economic anomalies. The weeklychange in the price of the mask in a town is given below.Time March1 stweekMarch2 ndweekMarch3 rdweekMarch4 thweekApril1 stweekApril2 ndweekApril3 rdweekApril4 thweekMay1 stweek Price(₹)100 150 175 200 200 175 70 50 50Forecast the Expected price of Mask for the 2 nd week of May.Strangely the Actual price of Mask in the city in the 2 nd week of May was 10 rupees.State at least four economic reasons why your calculated Expected price of Mask didnot match the Actual price of Mask.Now assume when the price of the Mask was 200 rupees per unit 5 lakh customersbought it. As the price reduced to 50 rupees only 1 lakh customers bought it.Calculate elasticity of demand and state which laws of economics are violated in thiscase, and how?