Stowe Automotive is considering an offer from the country of Indula to build a plant making automotive parts for use there. In preparation for a nal decision, Stowe's economists have been hard at work constructing a basil: econometric model for Indula to aid the company in predicting future levels of economic activity. Because of the cyclical nature of the automotive parts industry, forecasts of future economic activity are quite important in Stowe's decision process. Corporate prots (H _ 1) for all rms in Indula were about $110.00 billion. GDP for the nation is composed of consumption, C, investment, I, and government spending, G. It is anticipated that Indula's federal, state, and local governments will spend in the range of $220 billion next year. On the basis of an analysis of recent economic activity in Indula, consumption expenditures are assumed to be $154.00 billion plus 80% of national income. National income, Y, is equal to GDP minus taxes, T. Taxes are estimated to be at a rate of about 30% of GDP. Finally, corporate investments have historically equaled $33 billion plus 90% of last yeasz corporate profits (H _1). Construct a veeequation econometric model of the state of Indula. A Simple Model aflhe National Economy of Indula C: v I: v T: v GDP: 7 Assuming that all random disturbances average to zero, solve the system of equations to arrive at next year's forecast values for C, I, T, GDP, and Y. (Hint: It is easiest to start by solving the investment equation and then working through the appropriate substitutions in the other equations.) Forecast (Billions of dollars) C V I l T ; GDP V Y V Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w = 0.9 and w =0.3). (Note: The exponential smoothing forecasts may be begun by assuming Yt + 1 = Yt.) Moving Average Exponential Smoothing Year Actual Demand (5-year) (3-year) (W = 0.9) (W = 0.3) 2000 1,500 2001 1,545 2002 1,585 2003 1,620 2004 1,650 2005 1,675 2006 1,695 2007 1,710 2008 1,720 2009 1,725 2010 * The following table shows the square errors, (Y - Yt - 1 ) , for forecasts from 2005 through 2009. Fill the table by calculating the root mean square error (RMSE) for each of the methods.Fill the table by calculating the root mean square error (RMSE) for each of the methods. Square Error Moving Average Exponential Smoothing Year (5-year) (3-year) (W = 0.9) (W = 0.3) 2005 9,025 3,249 784 7,569 2006 6,400 2,209 529 6,561 2007 4,225 1,369 289 5,184 2008 2,500 729 144 3,600 2009 1,225 289 36 2,209 RMSE Based on the RMSE criterion, which of the forecasting methods is the most accurate? O Three-year moving average O Exponential smoothing (w = 0.3) O Five-year moving average O Exponential smoothing (w = 0.9)The economic analysis division of Mapep Enterprises has estimated the demand function for its line of weed trimmers as QB : 18,000 + 0.4N 7 3503.; + 903 where N is the number of new homes completed in the primary market area, PM is the price of the Mapco trimmer, and P, is the price of its competitor's Surere trimmer. In 201?, 1,500 new homes are expected to be completed in the primary market area. Mapcp plans to charge $65.00 for its trimmer. The Surere trimmer is expected to sell for $65.00. Under these conditions, the sales forecast for 2017 is V . If its competitor cuts the price of the Surere trimmer to $60.00, Mapco's sales will V by V . A 30% reduction in the number of new homes completed would V Mapco's sales by V . (Note: Ignore the impact of the price cut of the Surefire trimmer.)