ISYE 6402 ISYE 6402 Final Exam

ISYE 6402 / ISYE 6402 Final Exam (Latest Update 2025 / 2026) Time Series Analysis | Questions & Answers | Grade A | 100% Correct - Georgia Tech

Question:

Is ARCH-GARCH a good candidate for a fat-tailed QQ plot?

Answer:

Ideally GARCH models are fit to residuals that don't have fat tails in their Q-Q plot. There are other better models for this case.

Question:

Is any MA model stationary?

Answer:

Yes

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Question:

what's the TS function do?

Answer:

make a TS

Question:

You want to model both the contemporaneous and lagged relationships among several time series. A VAR model with added deterministic components is best suited for this task.False. We will need to use a SVAR for this task. The deterministic components capture seasonality or trend, not contemporaneous relationships.

Answer:

Another extension of the VAR model is the so-called structure VAR model.Which is an extension of the VAR model by considering a linear transformation of Yt through the A times Yt on the left. And or a linear transformation of the error term through the term B times epsilon t. The structural VAR models not only models lag temporal relationships, but also contemporaneous dependencies between the time series through the transformation matrices A and B.

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Question:

why do we need TS models?

Answer:

But why do we need yet another set of statistical modeling tools to model time series data?The main reason is that the time series response data are correlated. This correlation results in a much smaller number of degrees of freedom than otherwise assumed under independence.Moreover, because of the correlation, the data are concentrating to a smaller part of the probability space where the data align.Ignoring dependence leads to inefficient estimates of the parameters in a model. It leads to poor predictions, to standard errors unrealistically small. In other words, leading to narrow confidence intervals, thus, improper statistical inferences. In time series analysis provided in this course, we'll focus on several objectives.

Question:

the restrict function and VAR()

Answer:

The restrict() function in R selects the independent variables in the restricted VAR model through dropping statistically insignificant coefficients in the full model. It is the preferred model selection approach over a model selection using AIC or BIC. False. In the River Flow case study, it has been mentioned that dropping insignificant coefficients is not the correct approach for model selection. One should keep this in mind when using the restrict() function.

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