QUESTION 1The ACF for a data series is high until lag 3 then starts declining smoothly after lag 3The PACF for that data series is high until lag 4 then starts declining smoothly after lag 4What models would be a reasonable starting point based on these patterns?O An AR model with up to 4 AR terms, no MA componentO an ARMA model with up to 4 AR terms and up to 3 MA termsO An MA model with up to 3 MA terms, no AR componentO An ARMA model with up to 3 AR terms and up to 4 MA termsQUESTION 2Assume that we are comparing three different models: model 1, model 2, and model 3• The AIC for model 1 is -10, the BIC is -11• The AIC for model 2 is -8, the BIC is -12• The AIC for model 3 is -7, the BIC is -9Which of the following statements is trueO Model 3 fits the data much better than model 1 and model 2. We always want the AIC and the BIC to be highO AIC prefers model 1, BIC prefers model 2, both models should be kept as potential alternatives depending on the researcher's preferencesAIC prefers model 2, BIC prefers model 1, both models should be kept as potential alternatives depending on the researcher's preferencesO AIC prefers model 1, BIC prefers model 2, we should only use model 1 because the AIC is the only way to select a model

Since you have asked multiple question, we will solve the first question for you. If you want any…

QUESTION 1 The ACF for a data series is high until lag 3 then starts declining smoothly after lag 3 The PACF for that data series is high until lag 4 then starts declining smoothly after lag 4 What models would be a reasonable starting point based on these patterns? An AR model with up to 4 AR terms, no MA component an ARMA model with up to 4 AR terms and up to 3 MA terms O An MA model with up to 3 MA terms, no AR component O An ARMA model with up to 3 AR terms and up to 4 MA terms

QUESTION 1 The ACF for a data series is high until lag 3 then starts declining smoothly after lag 3 The PACF for that data series is high until lag 4 then starts declining smoothly after lag 4 What models would be a reasonable starting point based on these patterns? An AR model with up to 4 AR terms, no MA component an ARMA model with up to 4 AR terms and up to 3 MA terms O An MA model with up to 3 MA terms, no AR component O An ARMA model with up to 3 AR terms and up to 4 MA terms

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Use the data below to solve for the following: 2. Naïve method 3. Unweighted 3 month moving average 4. Weighted Moving Average (3 months, with the following weights: 0.45, 0.35, 0.20) 5. Simple Exponential Smoothing with the following parameters: Ft+1=32 and the α = .15 6. Time Series Regression Months 1 2 3 4 5 6 7 8 9 10 11 12 Firm's Actual Market share 30 32 33 34 28 33 29 27 32 33 28 33 7. Which of the following forecasting method will you use? 8. What are your basis in choosing for your forecasting techniques? Explain briefly. ONLY DO 56
PLEASE DO THIS BY EXCEL AND COMPLETE SOLUTION AND FORMULAS. I WILL UPVOTE THANK YOU
150 30 0 www ii) 14 i) 21 ii) 28 days 35 42 (a) The Figure above shows simulated data on the number of people (in thousands) who use public transport every day in Melbourne. i) 49 56 Does this time series exhibit a time trend? If there is a time trend, explain what could explain this trend. (b) Consider the following estimated model of industrial production (Y), which uses quarterly data from 2001q1 to 2022q4 and includes a constant, a time trend (t) and 3 seasonal dummy variables (Q1, Q2, and Q3) in the equation. Does this time series exhibit seasonality? What statistical test could you perform to test whether there is seasonality? If there is seasonality, explain what could explain this seasonal pattern. log(Y) = 6.01 +0.05t -0.0201 -0.03Q2 -0.0103 Interpret the estimated coefficients for the time trend and three seasonal dummies. Based on the estimated equation above, what would be the forecast for industrial production for the first quarter of 2024?
Cardiologists use the short-range scaling exponent α1, which measures the randomness of heart rate patterns, as a tool to assess risk of heart attack. The article “Applying Fractal Analysis to Short Sets of Heart Rate Variability Data” compared values of α1 computed from long series of measurements (approximately 40,000 heartbeats) with those estimated from the first 300 beats to determine how well the long-term measurement (y) could be predicted the short-term one (x). Following are the data (obtained by digitizing a graph). Short Long 0.54 0.55 1.02 0.79 1.4 0.81 0.88 0.9 1.68 1.05 1.16 1.05 0.82 1.05 0.93 1.07 1.26 1.1 1.18 1.19 0.81 1.19 0.81 1.2 1.28 1.23 1.18 1.23 0.71 1.24 Note: This problem has a reduced data set for ease of performing the calculations required. This differs from the data set given for this problem in the text. Find a 95% confidence interval for the mean long-term measurement for those with short-term measurements…
Cardiologists use the short-range scaling exponent α1, which measures the randomness of heart rate patterns, as a tool to assess risk of heart attack. The article “Applying Fractal Analysis to Short Sets of Heart Rate Variability Data” compared values of α1 computed from long series of measurements (approximately 40,000 heartbeats) with those estimated from the first 300 beats to determine how well the long-term measurement (y) could be predicted the short-term one (x). Following are the data (obtained by digitizing a graph). Short Long 0.54 0.55 1.02 0.79 1.4 0.81 0.88 0.9 1.68 1.05 1.16 1.05 0.82 1.05 0.93 1.07 1.26 1.1 1.18 1.19 0.81 1.19 0.81 1.2 1.28 1.23 1.18 1.23 0.71 1.24 Note: This problem has a reduced data set for ease of performing the calculations required. This differs from the data set given for this problem in the text. A. Compute the least-squares line for predicting the long-term measurement from the short-term measurement.…
17.6 please
Example 3.6 Assuming that the life of a given subsystem, in the wear-out phase, is normally distributed with u an operating time of 500 hours given that (a) the age of the component is 9,000 hours, (b) the age of the component is 11,000 hours. 10,000 hours and o = 1,000 hours, determine the reliability for CS Scanned with CamScanner
Time Period 1 2 3 4 5 6 7 8 Y₁ 200 210 215 216 219 220 225 226 Ŷ 200 e₁ |||||||| Please do all calculations with the accuracy of one decimal digit. 1) Calculate the forecast for period 9, using a five-period moving average MA(5), 2) If simple exponential smoothing with a smoothing constant of alpha=0.4, and the initial forecast 200 is used, what is the forecast for time period 4? 3) If simple exponential smoothing with a smoothing constant of alpha=0.4, and the initial forecast 200 is used, what is the forecast for time period 9? 4) Calculate MSE errors for both of the methods. Which method gives a smaller value of the MSE error?
Page 1: 1 Page 2: 2 Page 3: 3 Page 4: 4 Page 5: 5 Page 6: logwage Bo+female + educ+ & female educ Which diagram correctly specifies the following hypotheses &<0 and 0 wage wage Men & Women 1 (A) Men education Women (B) Men Women education Women Men
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…
State at least one conclusion from your data/graph (For example: State X has lost population by 10000 people, Region X is grew 10% over the last 50 years, etc.)
Question 1 part D