Consider the following ARMA model for{y,}:PΣa+E +bj=11=1where {e } is the residual.Which of the following assumption(s) on the residuals {ɛ } is needed to enable forecasting with this model in practice?O a. e is normally distributed.Ob.e is mean-independent of y,1,-2"*and e1-1'C.and eare stochastically independent for all +s:O d. All of the above.QUESTION 2How can the validity of the assumption(s) identified in Question 1 be examined in practice?O a. Use the Breusch-Pagan test, where the null hypothesis is that the distribution is normal.b.Use the Ljung-Box test, where the null hypothesis is that the first K autocorrelations are zero.Examine the SACF and SPACF of the estimated residualsd. Both (b) and (c).

Given the ARMA model for yt : yt=a0+∑j=1pajyt-j+εt+∑l=1qblyt-l where εt is the residual.

Answered: Consider the following ARMA model 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

See similar textbooks

Similar questions

69 Q1. [6 points] :From the following data set 3\ 9. 1 6. 5. 4 11 16 1.find correlation coefficients 2. Estimate the simple linear regression model 3. Find the predicted value of y when x-10
Calculate the forecasted values of tempurature based on weeks using Moving Average with m = 3. Required: Complete the rest of the table: Week Tempurature Predicted |e| e^2 1 10 - - - 2 14 - - - 3 8 - - - 4 25 29 5 16 6 22 7 14 8 35 9 15 10 27 11 18 12 40 13 28 14 40 15 25 16 65 Calculate the MAE: Calculate the MSE: In which week was forecasted tempurature the furthest from the actual tempurature? Predict what the tempurate would be for Week 17
There is a relationship between the following variables as y = 1 / (a * x ^ b) (x ^ b: means x over b). a) Calculate the correlation coefficient and interpret the degree of the relationship? b) Estimate the y value for x = 4.3 and the x value for y = 0.90 by obtaining the regression equation. x 1.2 1.5 2.0 3.0 4.0 5.0 y 0.58 0.50 0.32 0.18 0.15 0.10 x 1.2 1.5 2.0 3.0 4.0 5.0 y 0.58 0.50 0.32 0.18 0.15 0.10
11. In this same article on sleep duration and start time, researchers also considered whether school start time was related to obtaining an adequate amount of sleep. An adequate amountbof sleep was considered at least 8.5 hours of sleep, as recommended by the National Sleep Foundation. The authors used logistic regression models to associate the probability of adequate sleep to school start time. Here are some adapted logistic regression results from this study: In(odds of adequate sleep) = 6, + B,, where x1 = school start time, measured as the number of minutes after 7 AM that the school starts. For this model, B,-0.014, SE(B,)-0.005. - What Is the estimated odds ratlo of adequate sleep, and 95% CI, for students who start at 8:30 AM compared to those who start at 7:30 AM? a. 1.01 (1.00, 1.02) b. 1.52 (1.13, 2.05) C. 2.32 (1.27, 4.22) d. 4.05 (1.49, 11.02)
Assume a multiple linear regression y = Bo + B1 a1+ B2x2 + e. Which statement(s) is(are) true about the variance inflation factors (VIFS) of the coefficient estimates b1 and b2 ? I. The VIF of b, is the same as the VIF of b2. II. VIF will likely be large if X2 is highly positively correlated with X1 II. VIF will likely be large if X2 is highly negatively correlated with X1 IV. VIF will likely be close to 1 if X1 and X2 are independent O l and IV 1, II, III and IV Il and III OIV only I only
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain
Consider a simple regression Y = B1 + B2 X + u. Suppose we found out that the variance of error term is changing with larger values of X (heteroscedasticity). Show how you overcome the problem of heteroscedasticity by using White’s heteroscedasticity consistent variances (only for variance of the slope estimate). Show and explain.
when using delta method to calculate variance, shouldn't [g'(μ)]^2 equal to (θ+1)^4?
Use the following data for parts (a) through (e). x 5 7 3 16 12 9 y 8 9 11 27 15 13 Determine the equation of the least squares regression line to predict y by x. y^ = (value rounded to 4 decimal places ?) + (value rounded to 4 decimal places ?) x
A correlation coefficient might not have an appropriate relationship between two variables. This can happen between two variables when A. it is highly scattered B. is nonlinear is some way C. has a directionality problem
We are researching the charity donation behaviour of Australians. We have the following model: (E1) Donate = β0 + β1Income +β2Avg_Gift + β3Edu + u Where: Donate is a dummy variable equal to 1 if the individual makes a donation in response to a social media campaign by our charity organisation, and 0 otherwise Income is the annual household income Avg_Gift is the average value of past donations made by the individual to our charity Educ is the individual's level of education (in years) Our data is a random sample of the population and we have 1,614 observations. You should assume E [u | Income, Avg_Gift, Educ] = 0. Using the information above, please answer the following 3 questions. [i] Referring to Model (E1) above, interpret the coefficient β1. [ii] You know that this model will suffer from heteroskedasticity. Why is this the case? Explain your reasoning. [iii] In your own words, what is heteroskedasticity?

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS