[STUDY GUIDE] • HIGH-YIELD PRACTICE & REVIEW EDITION
ISyE 6402 Midterm Prep Exam Questions &
Verified Rationalized Answers, Updated
2026/2027 Edition
Comprehensive Examination Question Bank • In-Depth Rationales • Concept Mapping
TOTAL QUESTIONS EXAM TOPICS RATIONALES
66 Questions 11 Modules 100% Verified
DOCUMENT OVERVIEW
This document contains 66 verified questions with correct answers and explanations focused on time series
analysis concepts. It is suitable for students preparing for exams in statistics or econometrics, offering a
comprehensive review of key principles in time series modeling, including autoregressive models and
stationary processes.
EXAM BLUEPRINT & TOPIC DISTRIBUTION
Systematic breakdown of subject domains and exam coverage.
Topic Module Scope & Core Focus Questions Share (%)
This topic covers the estimation and interpretation of Vector
VAR Models and Estimation Autoregressive (VAR) models. 6 Qs 9.1%
This topic examines concepts of stationarity in time series and
Stationarity and Non-Stationarity implications of non-stationarity. 6 Qs 9.1%
This topic focuses on the properties and characteristics of
Autoregressive Processes autoregressive (AR) processes. 6 Qs 9.1%
This topic describes the properties of white noise processes
White Noise Processes and their role in time series. 6 Qs 9.1%
Cointegration and Long-Run This topic explores concepts of cointegration and their
Relationships significance in time series analysis. 6 Qs 9.1%
This topic discusses the relationships between correlation,
Correlation and Independence independence, and covariance in random variables. 6 Qs 9.1%
This topic addresses the characteristics and identification of
Structural VAR Models Structural VAR models. 6 Qs 9.1%
Confidential • Student Study Edition • Practice & Review Guide Page 1 of 20
,STUDENT STUDY & MASTERY EDITION PRACTICE & REVIEW GUIDE
This topic includes statistical tests relevant for analyzing time
Statistical Tests in Time Series series data, such as the t-test and ADF test. 6 Qs 9.1%
This topic examines the application of Autocorrelation
Function (ACF) and Partial Autocorrelation Function (PACF)
ACF and PACF Analysis in time series. 6 Qs 9.1%
Lagged Relationships and This topic investigates the lagged relationships between
Dynamics variables in time series data. 6 Qs 9.1%
This topic focuses on the diagnostic checks necessary for
Model Diagnostics validating time series models. 6 Qs 9.1%
Total Exam Coverage 11 Integrated Topic Modules 66 Qs 100.0%
Confidential • Student Study Edition • Practice & Review Guide Page 2 of 20
, STUDENT STUDY & MASTERY EDITION PRACTICE & REVIEW GUIDE
TOPIC 1: VAR MODELS AND ESTIMATION
6 Questions • 9.1% of Exam • This topic covers the estimation and interpretation of Vector Autoregressive (VAR) models.
QUESTION 1
Getting a 3 variable VAR model from summary(model) output of a VAR(1) model
Figure Q1: Reference Image
Correct Answer: First matrix: first row are coefficients for Xt1, second row are coefficients for Xt2, etc...
second matrix is Xt-1, i b/c this is a VAR(1) model last matrix are the constants eta_t is covariance matrix,
direct copy.
QUESTION 2
(c) Based on the fitted model, is there contemporaneous cross-correlation? Is there
lagged cross-correlation? Is there lagged auto-correlation? Explain.
Figure Q2: Reference Image
Correct Answer: Contemporaneous cross-correlation is NOT present if the variance-covariance matrix is a
diagonal matrix there is lagged correlation if the order p of the VAR(p) model > 0.
Confidential • Student Study Edition • Practice & Review Guide Page 3 of 20
ISyE 6402 Midterm Prep Exam Questions &
Verified Rationalized Answers, Updated
2026/2027 Edition
Comprehensive Examination Question Bank • In-Depth Rationales • Concept Mapping
TOTAL QUESTIONS EXAM TOPICS RATIONALES
66 Questions 11 Modules 100% Verified
DOCUMENT OVERVIEW
This document contains 66 verified questions with correct answers and explanations focused on time series
analysis concepts. It is suitable for students preparing for exams in statistics or econometrics, offering a
comprehensive review of key principles in time series modeling, including autoregressive models and
stationary processes.
EXAM BLUEPRINT & TOPIC DISTRIBUTION
Systematic breakdown of subject domains and exam coverage.
Topic Module Scope & Core Focus Questions Share (%)
This topic covers the estimation and interpretation of Vector
VAR Models and Estimation Autoregressive (VAR) models. 6 Qs 9.1%
This topic examines concepts of stationarity in time series and
Stationarity and Non-Stationarity implications of non-stationarity. 6 Qs 9.1%
This topic focuses on the properties and characteristics of
Autoregressive Processes autoregressive (AR) processes. 6 Qs 9.1%
This topic describes the properties of white noise processes
White Noise Processes and their role in time series. 6 Qs 9.1%
Cointegration and Long-Run This topic explores concepts of cointegration and their
Relationships significance in time series analysis. 6 Qs 9.1%
This topic discusses the relationships between correlation,
Correlation and Independence independence, and covariance in random variables. 6 Qs 9.1%
This topic addresses the characteristics and identification of
Structural VAR Models Structural VAR models. 6 Qs 9.1%
Confidential • Student Study Edition • Practice & Review Guide Page 1 of 20
,STUDENT STUDY & MASTERY EDITION PRACTICE & REVIEW GUIDE
This topic includes statistical tests relevant for analyzing time
Statistical Tests in Time Series series data, such as the t-test and ADF test. 6 Qs 9.1%
This topic examines the application of Autocorrelation
Function (ACF) and Partial Autocorrelation Function (PACF)
ACF and PACF Analysis in time series. 6 Qs 9.1%
Lagged Relationships and This topic investigates the lagged relationships between
Dynamics variables in time series data. 6 Qs 9.1%
This topic focuses on the diagnostic checks necessary for
Model Diagnostics validating time series models. 6 Qs 9.1%
Total Exam Coverage 11 Integrated Topic Modules 66 Qs 100.0%
Confidential • Student Study Edition • Practice & Review Guide Page 2 of 20
, STUDENT STUDY & MASTERY EDITION PRACTICE & REVIEW GUIDE
TOPIC 1: VAR MODELS AND ESTIMATION
6 Questions • 9.1% of Exam • This topic covers the estimation and interpretation of Vector Autoregressive (VAR) models.
QUESTION 1
Getting a 3 variable VAR model from summary(model) output of a VAR(1) model
Figure Q1: Reference Image
Correct Answer: First matrix: first row are coefficients for Xt1, second row are coefficients for Xt2, etc...
second matrix is Xt-1, i b/c this is a VAR(1) model last matrix are the constants eta_t is covariance matrix,
direct copy.
QUESTION 2
(c) Based on the fitted model, is there contemporaneous cross-correlation? Is there
lagged cross-correlation? Is there lagged auto-correlation? Explain.
Figure Q2: Reference Image
Correct Answer: Contemporaneous cross-correlation is NOT present if the variance-covariance matrix is a
diagonal matrix there is lagged correlation if the order p of the VAR(p) model > 0.
Confidential • Student Study Edition • Practice & Review Guide Page 3 of 20
