Univariate time series models are especially useful when it comes to forecasting. Consider the following MA(1) process: yt = 0.5ut-1 + ut 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 (vt}?
Univariate time series models are especially useful when it comes to forecasting. Consider the following MA(1) process: yt = 0.5ut-1 + ut 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 (vt}?
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Transcribed Image Text:Univariate time series models are especially useful
when it comes to forecasting. Consider the
following MA(1) process: yt = 0.5ut-1 + ut 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 {vt}?
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