Compute the forecasted values for Yt for July and August in 2020 by using the modelsstated in (c) and (d) Question 3Given two time series for the year 2020:Month JanuaryFebruaryMarchApril0.679May1.233JuneY,-1.049-1.0750.309-0.560111 (c) The following ARIMA(0,0,1) model:Y; = -0.7 + & + 0.5ɛ-1(d) The following dynamic regression:0.7Y;1- 0.2BXŁ-2 – 0.5Y;–1 + E;

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Answered: Question 3 Given two time series for…

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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The following table contains yearly data on the price of beef from 2022 to 2024: Year Price of Beef ($/Kg) 3 5 8 2022 2023 2024 Forecast the price of chicken for 2025 using the following methods: 1. A 3-Year moving average method; 2. A simple exponential smoothing method (use a=0.5); 8. A simple linear trend model.
Please show work in excel Stars Co. wants to advertise its products in the hope that more advertising will result in more sales. The following data have been collected in the past year showing money spent on advertising in a month and sales on the same month: a) Develop a regression equation to forecast the Sales as a function of the Advertising by drawing a scatter chart in Excel. b) Suppose that the company would like to advertise in the amount of 30 thousands. Estimate the Sales in response to this amount of advertising. Month Sales (Thousands) Advertising (Thousands) Jan 1200 30 Feb 1100 25 March 1220 32 April 990 23 May 1190 31 June 1050 24 July 770 22 Aug 1080 28 Sept 1220 33 Oct 1140 25 Nov 740 19 Dec 790 20
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.
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The coefficients in a distributed lag regression of Y on X and its lags can be interpreted as the dynamic causal effects when the time path of X is determined randomly and independently of other factors that influence Y. Explain How?
If a forecaster wanted to choose a forecasting model that avoided having a large forecasting error in any given period, which error metric should they use when evaluating their forecasting options? CFE O AFE MAD MAPE O MSE
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.)
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Question 3 Given two time series for the year 2020: Month January February March April 0.679 May 1.233 June Y, -1.049 -1.075 0.309 -0.560 1 1 1