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E.1) Suppose we model the data (not necessarily any of the series in Question A1) as an A(1) and get the following estimated equation: Yt = 0.5u;-1+u; What is this MA(1) process' theoretical ACF at lag 1? At lag 2? Discuss the ACF and PACF of general MA, AR, and ARMA processes. Why are we interested in measures like ACF? In univariate time series modelling, how can measures like ACF and PACF be used? Discuss in practice how to select the optimal lag length in time series modelling.

E.1) Suppose we model the data (not necessarily any of the series in Question A1) as an A(1) and get the following estimated equation: Yt = 0.5u;-1+u; What is this MA(1) process' theoretical ACF at lag 1? At lag 2? Discuss the ACF and PACF of general MA, AR, and ARMA processes. Why are we interested in measures like ACF? In univariate time series modelling, how can measures like ACF and PACF be used? Discuss in practice how to select the optimal lag length in time series modelling.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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(A2.1) Suppose we model the data (not necessarily any of the series in Question Al) as an MA(1) and get the following estimated equation: Yt= 0.5u:-1+u; What is this MA(1) process' theoretical ACF at lag 1? At lag 2? Discuss the ACF and PACF of general MA, AR, and ARMA processes. Why are we interested in measures like ACF? In univariate time series modelling, how can measures like ACF and PACF be used? Discuss in practice how to select the optimal lag length in time series modelling. (A2.2) Univariate time series models are especially useful when it comes to forecasting. Consider the following MA(1) process: Ye = 0.5u;–1+u: What is your forecast for yt+1 if you observe Ut-1 = 0.2 and ut = -0.8? What is your forecast for yt+2? What is the forecast for 10-step ahead? How does the forecast for the distant future compare to the unconditional expectation of this MA(1) process? How is the forecasting exercise related to the expectation of the stochastic process {yt}?
Transcribed Image Text:(A2.1) Suppose we model the data (not necessarily any of the series in Question Al) as an MA(1) and get the following estimated equation: Yt= 0.5u:-1+u; What is this MA(1) process' theoretical ACF at lag 1? At lag 2? Discuss the ACF and PACF of general MA, AR, and ARMA processes. Why are we interested in measures like ACF? In univariate time series modelling, how can measures like ACF and PACF be used? Discuss in practice how to select the optimal lag length in time series modelling. (A2.2) Univariate time series models are especially useful when it comes to forecasting. Consider the following MA(1) process: Ye = 0.5u;–1+u: What is your forecast for yt+1 if you observe Ut-1 = 0.2 and ut = -0.8? What is your forecast for yt+2? What is the forecast for 10-step ahead? How does the forecast for the distant future compare to the unconditional expectation of this MA(1) process? How is the forecasting exercise related to the expectation of the stochastic process {yt}?
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